How to define and examine implicit processes
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论证是一门学问(Argumentation is a branch of learning)Hint: there is a critical plot to revealI. Basic Rule General RulesRule 1 determine premises and conclusionsRule 2 straighten out your ideas (i.e. progressive meaning, even if there is sufficient evidence, but also not incoherent)Rule 3 proceed from a reliable premiseRule 4 specific and concise (not empty words). In this section, the tragedy of Parsons (Talcott Parsons) brother was mercilessly brush a meal, and his one hundred and thirty words were the author in thirteen words reported again. Mills seems to have painted Parsons in the book of sociological imagination, tooRule 5 gives practical reasons, not just radical statements (which are needed in the current Chinese language environment). There is a classic phrase in this passage: "generally speaking, if you can't imagine why someone will believe the point you refute, then you probably don't understand it yet." The author has a suggestion that "usually people support a view that is serious and heartfelt.". Try to analyze their point of view - try to understand their reasons - even if you don't agree with them at allRule 6 speak the same way (try to use the same words to refer to one thing, and not to play with it. Hugh ah, please use adifferent way of expressing the temptation to resist. The use of the same expression can make layers of progressive relations more obvious. In this section the author gave a good example, he first gives an expression: "when you learn about other cultures, you begin to realize the diversity of human customs. This new understanding of the diversity of social mores may make you start to appreciate other ways of life. Therefore, studying anthropology may well make you more tolerant." Then he gave another expression: "when you understand other cultures, you begin to realize the diversity of human customs.". When you begin to realize the diversity of human customs, you are likely to become more tolerant. Therefore, when you understand other cultures, you are likely to become more tolerant." The author believes that the latter expression is better than the former, and I think so. Although a single narrative can make us lose some of the complexity of things, we can, on the whole, tolerate it for the convenience of othersTwo. Summarize (some arguments are summarized by an example or several examples, which should be called an example) GeneralizationsRule 7 don't just give an exampleRule 8 uses a representative exampleRule 9 to probability events or background note (social psychology tells us that human beings will understand the outside world and others, tend to take a shortcut, make the fundamental attribution error will be the behavior of others due to their nature, their behavior is attributed toenvironmental restrictions. Therefore, in examining a factor, we need to see the status or general performance of this factor in the backgroundRule 10 treat statistics carefullyRule 11 consider the evidence of a counterexample (that is, evidence that does not support your point of view). We should search for counter evidence systematically and purposefully, which is a magic weapon for us to get rid of our opinions and to think more deeply and seek deeper lawsThree, analogical argument (by analogy) Arguments, by, AnalogyRule 12 to do comparison, examples must be similar in nature (how to know the nature of similar similarity is indeed a problem, therefore, in analogy reasoning must be very careful about that, in general, each category, scale far wider, its feasibility will do analog line or acceleration decreased to yes)Four. Source of information SourcesRule 13 lists the sources of informationRule 14 looks for reliable sources (we should give a description of our references, which will help increase our persuasiveness)Rule 15 looks for a fair source of information (some sources may indeed know it, but it may not be reliable. Just the general source of information has the following features: independence;would not spend time with opponents or comments to belittle others extreme or simplistic; it will admit responsibility on the other side of the argument and evidence, and one by one).Rule 16 verify the sources in many waysRule 17 use the Internet carefullyFive, causal argument Arguments, about, CausesRule 18, causal arguments begin with Association (i.e., there is a significant correlation between cause and effect, and as to what makes a significant correlation, I still need to resort to statistics)Rule 19 an association may have multiple interpretations (some only a coincidence; even if there are related, simply can not determine who know who fruit; even if there is likely to be related, related parties behind the deeper factors forcing parties in homomorphic manipulation. For example, the extent of soft pitch on the playground is significantly related to the increase in accidental drowning, and the reason? Even if there is correlation, it may be a causal relationship; even if there is correlation, it cannot be said to be absolutely decisive, because there may be other reasons to influence it at the same timeRule 20 find the most likely explanation First, we need to make the association complete. That is to say, we should try to make clear the intermediate link between the result and the fruit; two, to look for more information, to evaluate the influenceof the related parties carefully, and to choose the more influential oneRule 21. Situations can be complicated sometimes. There are probably many reasons for being here or talking about something. The authors cite an example, say, that a leads to B, and then prove that a sometimes does not lead to B, or that C may sometimes lead to B alone, which is not sufficient to constitute a counterexample, and that a is completely negated and that the B statement is incorrect. In fact, the reverse is the sameSix, deductive demonstration, Deductive, ArgumentsRule 22 ponens (Modus Ponens the following rules are not only that what flower heads, the general form of deductive reasoning. This is the first kind: if a, then B. Presence of A. So B. It should be noted that the premise of correctness in deductive reasoning is crucial, and that mistakes must be wrong later onRule 23 negates the latter form Modus Tollens (the second form of deductive argument, that is, if a, then B. B does not exist, then non a. Holmes once had the inference that "there was a dog in the corridor, but although someone entered the corridor and led a horse, it did not."...... Obviously,...... The comer is a man quite familiar with the dog. "Rule 24 Hypothetical syllogism (hypothetical syllogism deductive third forms: if a, then B. If B, then C. So, if a, then c)Rule 25 Disjuncitve syllogism (disjunctive syllogism deductivefourth forms: A or B. B does not exist. So, a. Here, a and B should be mutually exclusive and exhausted. The author quoted Russell's reasoning as an example: "the hope of progress or the improvement of morality, or the increase of wisdom.". Moral improvement is not easy. Therefore, the hope of progress must be given to the increase of wisdomRule 26, two difficult reasoning Dillema (deductive argument in fifth forms: A or B). If a exists, then C exists. If B exists, then d exists. So, either C or D. The author cites Schopenhauer's famous remark: "the closer the two hedgehogs are, the more likely they are to stab each other, but if they are separated from each other, they feel lonely.". So it is with human beings." Note that a and B should be mutually exclusive, too.)Reductio ad absurdum (rule 27 reduction to absurdity assumption on the contrary, this assumption is derived from the fallacy. So we can only accept the original hypothesis. For example, David Hume's argument for God: if God exists, then the world should be perfect. Because the world is not perfect, there is no GodRule more than 28, step by step, deductive argument (the blending of the above deductive argument patterns)Seven, to demonstrate in detail that Extended Arguments (to do a finer point of argument, we need to pay attention to the following rules, which are summaries and abstractions of the rules above, which are expanded by time series)Rule 29. The topic of the argument (argument) is to take over a topic, not a position. Don't rush to use arguments to support your first position in your head. Even if we have to stick to a position, try to look for innovative new perspectives, partly because we may be surprised at what we are looking forRule 30 write down the basic ideas in the form of proof (in other words, don't be lazy before you make an announcement, honestly think all logical chains, or write them out)Rule 31 make a specific demonstration of the basic premises (i.e., these premises need to be reviewed and documented)Rule 32 consider objections (we have to constantly ask ourselves, what argument can best refute your conclusions and evidence? The world is so complex that it seems to us that it is obvious,Others may not be so. They may be just as considerate, intelligent, and well intentioned as we areRule 33 consider other solutions (if the solution, including the present our argument we have to ask ourselves, there is no solution, other solutions compared to our own thought and other solutions is good or bad, there may be overlap)Eight, argumentative Argumentative Essays (how to write a paper)?Rule 34 (not long winded, straight to the point when you feel yourself, not long winded note that you show whether the viewis the most concise. For centuries, for example, philosophers have debated the best way to get happiness...... "It's a bad way to start. You're talking about things that everyone already knows. At the beginning of the article, you need to say something new, like, "in this article, I will prove that the best thing in life is freedom."Rule 35 put forward a definite opinion or suggestionRule 36, your argument is your outline (straight after we need to view further elaborated. First, make a generalization, that is, the logical chain of your basic argument. Let the reader clearly understand what we need to prove and how to prove it. Next, we should advance every premise of this basic argument in turn. In each section, the premise of the premise of the premise is followed by the premise that the proof is complete. In due course, remind the reader of what position the present argument plays in the overall argument and where it is directedRule 37 gives a detailed account of the objections, tries to rebut (i.e., to introduce the logical chain of the opponent in detail, his fears, and to admit the reasonableness of his argument or logic)Rule 38 collect and use feedback information (that is, try to find a critic)Rule 39 please be modest (if you don't have to admit the possibility of death, their errors exist)Nine, oral argument Oral ArgumentsRule 40 impress your audience (must show some enthusiasm, make personalized speech; respect, at the same time to the patient; the audience don't force them to change ideas; never let the audience feel to you as we stood there is a cut above others; because we have something to share, they sat there, either because they want to see, either because it is their work or study requirements. We want to thank others for listening and not wasting timeRule 41 fills you with a sense of scene (to watch your audience, with an expression and move positions)Rule 42 set a road sign for your argument (oral argument needs to provide more "signposts" than written argumentation, use more repetition; use pause to represent important transitions, let people have time to think)Rule 43 speak positively, and let your audience look forward, not just something that will elude you. Because the positive attitudes and feelings of the audience are often contagiousRule 44 use less visual aidsRule 45 at the end of the style should be (first, the end should be timely; secondly, do not hastily end, to be grand, to have style, there are points, flowery rhetoric)Note: it is not unheard of, but simple and practical, that argumentation is a Book of knowledge. It is not a standard of operation, not a requirement of value. But we often stumble oversimple, known problems, so it takes a day to extract it, make a feeling, and talk about it.。
Improving ones proficiency in scientific English involves a multifaceted approach that encompasses reading,writing,speaking,and listening skills.Here are some strategies to enhance your scientific English abilities:1.Reading Scientific Literature:Start by reading scientific articles,journals,and books in your field of interest.This will not only help you understand the subject matter but also familiarize you with the specific vocabulary and terminologies used in scientific writing.2.Expand Your Vocabulary:Make a list of scientific terms and definitions.Regularly review and try to use these terms in your daily conversations and writings to reinforce your understanding.3.Practice Writing:Write summaries of the articles you read or create your own research papers.This will help you understand the structure of scientific writing and improve your ability to express complex ideas clearly and concisely.4.Participate in Discussions:Engage in scientific discussions,either online or in person. This will help you practice your speaking skills and learn how to articulate your thoughts effectively.5.Listen to Scientific Talks and Podcasts:Listening to experts in your field discussing scientific topics can improve your comprehension and pronunciation of scientific terms.e Language Learning Tools:Utilize apps and online resources designed for language learning.Many of these tools offer specialized modules for academic and scientific English.7.Attend Workshops and Seminars:Look for opportunities to attend workshops, seminars,and conferences where you can learn from experts and practice your scientific English in a professional setting.8.Get Feedback:Share your written work with peers or mentors and ask for constructive feedback.This can help you identify areas for improvement and refine your writing skills.9.Learn Grammar and Syntax:A strong foundation in English grammar and sentence structure is essential for clear and effective scientific communication.Invest time in understanding the nuances of scientific writing.10.Stay Updated with Current Research:Keep up with the latest research in your field. This will not only broaden your knowledge but also expose you to new scientificlanguage and concepts.11.Practice Critical Thinking:Develop the ability to critically analyze scientific information.This skill is crucial for understanding complex scientific arguments and for constructing your own wellreasoned scientific arguments.e Dictionaries and Thesaurus:Regularly consult scientific dictionaries and thesauruses to understand the meanings and usage of scientific terms.13.Write Regularly:Make writing a habit.The more you write,the more comfortable you will become with expressing scientific ideas in English.14.Peer Review:Engage in peer review processes.This can provide you with insights into how others perceive your writing and areas where you can improve.15.Cultural Exchange:Interact with international scientists and students.This can expose you to different ways of thinking and speaking about science.By integrating these strategies into your routine,you can significantly improve your scientific English skills,making you a more effective communicator in the scientific community.。
表达不公平的现状和规则的具体例子英语作文全文共3篇示例,供读者参考篇1Title: The Unfairness in the Current Social System and RegulationsIn today's society, unfairness can be seen in various aspects of life. From unequal opportunities in education and employment to discriminatory practices based on race, gender, and social status, the issue of unfairness is a prevalent and damaging problem that needs to be addressed. In this essay, I will examine some specific examples of unfairness in our society and discuss how existing regulations often fail to address these injustices.One of the most glaring examples of unfairness in society is the wage gap between men and women. Despite equal qualifications and performance, women are consistently paid less than their male counterparts in many fields. This discriminatory practice is a clear violation of the principle of equal pay for equal work, yet it persists due to a lack of effective regulations and enforcement mechanisms. The existing lawsagainst gender discrimination in the workplace are often inadequate or poorly enforced, leading to continued disparities in pay and opportunities for women.Another example of unfairness can be seen in the criminal justice system, where people of color are disproportionately targeted, arrested, and incarcerated compared to white individuals. This systemic racism has deep roots in our society and is perpetuated by biased regulations and practices within law enforcement and the legal system. Despite efforts to address these disparities, such as the implementation of body cameras and implicit bias training for police officers, the fundamental issues of racial discrimination and inequality remain largely unaddressed.Furthermore, unfairness can also be observed in the educational system, where students from disadvantaged backgrounds face greater obstacles to academic success. Inadequate funding for schools in low-income communities, unequal access to quality teachers and resources, and biased standardized testing practices all contribute to the perpetuation of educational inequities. Despite the existence of laws such as the Equal Educational Opportunities Act, which prohibits discrimination in education based on race, gender, or economicstatus, these regulations have not been effectively implemented to address the systemic barriers to educational equity.In addition to these examples, there are many other instances of unfairness in society, such as discriminatory housing practices, unequal access to healthcare, and environmental injustices that disproportionately affect marginalized communities. The root cause of these injustices lies in the deep-seated inequalities and biases that exist within our social structures and institutions. While regulations and laws are intended to protect against discrimination and promote fairness, they often fall short of addressing the complex and intersectional nature of societal injustices.To truly address the issue of unfairness in society, we must work towards implementing comprehensive and inclusive regulations that take into account the diverse lived experiences and needs of all individuals. This requires a proactive approach to identifying and dismantling systemic barriers to social justice, as well as a commitment to holding institutions and individuals accountable for their discriminatory practices. By working together to challenge and change the status quo, we can move towards a more equitable and just society for all.篇2Title: Unfairness in Society: Examples and RulesIntroductionIn every society, there are instances of unfairness that exist due to various factors such as social status, economic background, or discrimination. These inequalities can be seen in different aspects of life, including education, healthcare, employment, and justice. In this essay, we will explore examples of unfairness in society and discuss the rules that can help address these issues.Examples of Unfairness1. EducationOne of the most common examples of unfairness in society is the unequal access to quality education. In many countries, students from wealthy families have better opportunities for education, including access to private schools, tutors, and resources. On the other hand, students from low-income families often struggle to afford tuition fees, books, and other essentials necessary for learning. This disparity in educational opportunities can lead to a cycle of poverty and inequality that is difficult to break.2. HealthcareAnother area where unfairness is prevalent is in healthcare. People with higher incomes and better access to resources are more likely to receive timely medical care, quality treatments, and services. In contrast, individuals from marginalized communities or lower-income households may face barriers to healthcare, including long wait times, limited access to specialty care, and inadequate facilities. This disparity in healthcare can impact the well-being and longevity of individuals, leading to preventable illnesses and premature deaths.3. EmploymentUnfairness in the workplace is also a widespread issue that affects many individuals. Discrimination based on gender, race, age, or disability can limit job opportunities for qualified candidates and create a hostile work environment. Additionally, unequal pay and lack of promotion opportunities for marginalized groups can perpetuate inequalities in income and wealth. These challenges can prevent individuals from achieving their full potential and hinder their professional growth and development.4. JusticeThe justice system is another area where unfairness is often observed, particularly in cases of racial profiling, biasedsentencing, and unequal treatment under the law. People from minority groups are more likely to be targeted by law enforcement, receive harsher penalties for the same crimes, and experience discrimination in court proceedings. These injustices can erode trust in the legal system and perpetuate systemic racism and inequality in society.Rules to Address UnfairnessDespite the challenges of unfairness in society, there are rules and regulations that can help address these issues and promote equality and justice for all individuals. Some of the rules that can be implemented include:1. Equal Opportunity LawsEnforcing equal opportunity laws that prohibit discrimination in education, healthcare, employment, and other areas can help ensure that all individuals have access to the same opportunities and resources. These laws can protect individuals from unfair treatment based on their race, gender, age, or disability and promote a more inclusive and diverse society.2. Social Welfare ProgramsImplementing social welfare programs that provide assistance to low-income families, individuals with disabilities,and other vulnerable populations can help reduce inequalities in society. These programs can include affordable housing, healthcare, food assistance, and job training initiatives that support individuals in need and empower them to improve their quality of life.3. Diversity and Inclusion PoliciesPromoting diversity and inclusion in the workplace through policies that support hiring, promotion, and retention of individuals from diverse backgrounds can help create a more equitable and supportive work environment. These policies can help companies recruit top talent from diverse populations, foster innovation and creativity, and promote a culture of respect and acceptance.4. Legal ReformAdvocating for legal reform that addresses systemic injustices in the justice system, including racial profiling, biased sentencing, and inadequate legal representation, can help promote fairness and equality under the law. Reforming outdated laws and policies that perpetuate inequalities can help ensure that all individuals receive equal treatment and protection under the law.ConclusionIn conclusion, unfairness in society is a pervasive issue that affects individuals from all walks of life. By recognizing the examples of unfairness in education, healthcare, employment, and justice, and addressing these issues through rules and regulations that promote equality and justice for all individuals, we can create a more just and equitable society for future generations. It is up to each one of us to work together to challenge unfairness and advocate for a world where everyone has the opportunity to thrive and succeed.篇3The issue of unfairness is prevalent in society, whether it be in the form of discrimination, inequality, or biased rules and regulations. In many cases, these unfair practices can perpetuate systemic disadvantages that hinder the opportunities and rights of certain individuals or groups. This essay will explore the current state of unfairness and provide specific examples of unfair rules and regulations that contribute to this problem.One area where unfairness is commonly observed is in the workplace. Despite advancements in diversity and inclusion initiatives, discrimination based on race, gender, age, or otherfactors still persists. For example, in many industries, there is a gender pay gap where women are paid significantly less than their male counterparts for the same work. This disparity is not only unfair but also perpetuates the cycle of gender inequality in the workforce.Another example of unfairness can be seen in the education system. Students from low-income backgrounds often face barriers to accessing quality education and resources, which can impact their academic success and future opportunities. In some schools, funding is disproportionately allocated to wealthier neighborhoods, resulting in disparities in educational quality and opportunities for students based on their socioeconomic status.Unfair rules and regulations also play a role in perpetuating inequality. For instance, in some countries, there are discriminatory laws that restrict the rights of certain marginalized groups, such as the LGBTQ+ community. These laws can prevent individuals from fully participating in society and accessing essential services, leading to further marginalization and discrimination.Furthermore, unfair rules and regulations can also impact marginalized communities in other ways. For example, in the criminal justice system, there are disparities in sentencing andtreatment based on race and socioeconomic status. Studies have shown that minority individuals are more likely to be arrested, charged, and sentenced to harsher penalties compared to their white counterparts for the same offenses. This systemic bias perpetuates racial inequalities and contributes to the overrepresentation of minorities in the criminal justice system.In conclusion, the prevalence of unfairness in society is a significant issue that continues to impact individuals and communities across various sectors. By highlighting specific examples of unfair rules and regulations, such as discrimination in the workplace, disparities in education, and biases in the criminal justice system, it is evident that there is a dire need for systemic change to address these inequalities. It is crucial for policymakers, institutions, and individuals to work towards creating a more just and equitable society where everyone has equal opportunities and rights. Only through collective effort and a commitment to fairness can we dismantle the barriers that perpetuate unfairness and create a more inclusive and equitable world for all.。
大学英语积极词汇所谓“大学英语积极词汇”即要求学生能够在认知的基础上在口头和书面表达两方面熟练运用的词汇,在一般要求(四级)阶段约为2000词,较高要求(六级)阶段约为2200词,更高要求(六级后)阶段约为2300词(实为2337词)。
重申一下,这些词不光对四六级有用,对考研、考博甚至英语专业考试都是有用的。
对这些词,不光要知道意思,还要知道其准确的读音、拼写、词性、词性变化、意义和用法。
应该制定一个计划,每天搞定多少个,一方面可以按字母表的顺序搞,另一方面在复习其他题型时遇到这些词可以特别留意,记住其搭配或例句。
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解决方法题型英语作文举例英文回答:In the face of complex problems, finding comprehensive solutions requires a systematic approach that addresses the root causes and encompasses a wide range of perspectives. One effective method for tackling such challenges is to employ a problem-solving framework that incorporates the following key steps:1. Define the Problem Clearly: Begin by precisely identifying the nature of the problem, its scope, and its potential impact. Conduct thorough research and gather data to ensure a comprehensive understanding of the issue.2. Analyze Root Causes: Delve deeper to uncover the underlying factors that have contributed to the problem. Consider both direct and indirect influences, as well as any systemic or environmental factors that may be at play.3. Generate Potential Solutions: Brainstorm a wide range of possible solutions, considering both short-term and long-term strategies. Explore innovative ideas and unconventional approaches, seeking input from diverse stakeholders.4. Evaluate and Select Solutions: Carefully assess the feasibility, effectiveness, and potential consequences of each proposed solution. Prioritize options based on their potential impact, cost-effectiveness, and alignment with the problem's root causes.5. Implement and Monitor: Put the selected solutions into action, providing adequate resources and support for effective implementation. Monitor the progress and outcomes regularly, making necessary adjustments along the way.6. Communicate and Engage: Keep stakeholders informed about the problem-solving process, sharing progress updates and seeking feedback. Engage with the community to foster understanding and support for the implemented solutions.By following this systematic framework, we can increase the likelihood of developing comprehensive and effective solutions that address the root causes of complex problems. It empowers us to make informed decisions, allocate resources efficiently, and foster collaboration among diverse stakeholders.中文回答:解决问题的方法:1. 明确问题,准确识别问题的性质、范围和潜在影响。
商务英语(BEC)考试高级阅读真题及答案题nPART ONEQuestions 1-8Look at the statements below and at the five reports about companies onX the opposite page from an article giving advice to self-employed consultants about negotiating fees for their services・Which book (A, B, C, D or E) dose each statement 1 - 8 refer to ?XFor each statement ! - 8, make one letter (A, B, C, D or E) on your Answer九Sheet .You wi11 need to use some of these letters more than once •入Example :0 Lack of self-confidence wil1 put you at a disadvantage in a negotiation .0 A B C D E1.This company has been involved in diversifying its business activities・2.Al though this company is doing wel1, it has a number of internal difficulties to deal with3.This company has reduced the profits it makes on individual items4.One statistic is a less accurate guide to this company" s performanee than another5.The conditions which have helped this company are likely to be less favourable in the future6.This company" s share price has been extremely volatile over the last twelve months7.This company is likely to be the subject of a takeover bid in the near future8.This company" s performance exemplifies a widely held beliefAChemi cal CompanyMasterson" s interim pre-tax profits growth of 20% was somewhat inflated as a result of the income received from the disposal of several of the company" s subsidiaries. The underlying 8% rise in operatingprofits is a more realistic gauge of the company" s true progress. However, Masterson" s impending merger with Bentley and Knight and its appointment of a new chief executive should mean that the company wi 11 be able to sustain growth for the foreseeable fut5ure. The share price has varied little during the course of the year and now stands at £ 6. 75BHotel GroupDuring the past year the Bowden Hotel Group has acquired 77 new properties, thus doubling in size . last week the group repor ted pre-tax prof i ts of £ 88 mi 11 ion in the firs t si x mon ths of the year, ahead of expectations and helped by a strong performance from its London-based hotels and newly expanded US business. However. There is still some way to go. Integration of the new acquisitions is still not complete and, while the share price has risen recently, major problems with integration have yet to be solved.ctopsage, comThis time last year a share in Usertech was worth just over £ 1. six mon ths ago it was worth £ 40. today it is priced at under £ 8 . if proof were need, here is an 订lustration of how much of a lottery the technology market can be. But some technology companics are fighting back and Usertech is one of them . What has renewed excitement in the company is the opening of its new American offices in Dallas and its ambitious plans to expand its user base in both North and Latin America.DBui Iding CompanyRenton' s share price has risen gradual 1 y over the past year from £2.4 to £ 3. 8 • The company has been successful in choosing prime location for its buildings and has benefited from the buoyant demand for housing・ Whilst this demand is expected to s 1 ow down somewhat during the next year, investors are encouraged by the company" s decision to move into building supermarkets. Work has already begun on two sites in London, and the company is expected to sign a contract within the next month for buiIding four large supermarkets in Scotland.Pottery ManufactuterMil ton Dishes has bee n through a shake-up over the past year ・ The group, which has bee n cutting marg ins and improvi ng marketing, may post a smal 1 prof i t t his year. The many members of the Mi 1ton family, who betwee n them own 58 per cent of the bus in css, have been watch ing the share price rise steadily and several are looking to sei 1. Trade rival Ruskin has bought up just over 17 percent of the shares and could well be spurred into further action by the signs of a recovery at the firm・[NextPage]答案:1D2B3E4A5D6C7E8C这种类型的题不过是一个句子换-•种说法,关键是看对每一•段话的理解。
用逻辑解决问题英文作文## Logical Problem-Solving ##。
English Answer:In the realm of critical thinking, logical problem-solving stands as a beacon of analytical acumen, empowering individuals to navigate complex challenges with precision and efficiency. This cognitive skillset involves a series of systematic steps that guide us toward logical conclusions and optimal solutions.1. Define the Problem: Clearly articulate the issue at hand and its key components.2. Gather Information: Seek relevant data and evidence to inform your analysis.3. Identify Patterns: Examine the available information to discern any underlying relationships or patterns.4. Formulate Hypothesis: Develop potential solutions or explanations based on the patterns identified.5. Test Hypothesis: Conduct experiments or gather additional evidence to evaluate the validity of your hypotheses.6. Draw Conclusions: Based on the test results, arrive at logical conclusions and determine the most likely solution.7. Communicate Findings: Clearly present your findings, supporting your conclusions with evidence and reasoning.Logical problem-solving is a valuable skill applicable across various domains, from scientific research to business decision-making. It fosters critical thinking, analytical reasoning, and the ability to navigate uncertainty with confidence.## 解决问题中的逻辑运用 ##。
how to define definition英文短文Defining a definition is the process of stating the meaning or essence of a word, concept, or phenomenon. It involves clearly and precisely explaining what something is and what it is not. A good definition should be accurate, concise, and widely applicable.One way to define a definition is to start with the genus, which is the broad category or class to which the thing belongs. For example, if you were defining a "dog," you might start by saying that it is a member of the genus "mammal." Then, you would add specific characteristics that distinguish the thing from other members of the genus. In the case of a dog, you might say that it is a domesticated animal that is typically kept as a pet and has a tail and four legs.Another important aspect of defining a definition is to use clear and specific language. Avoid using vague or ambiguous terms that could be interpreted in different ways. Instead, use words that have a precise meaning and that are widely understood. Additionally, it can be helpful to use examples or illustrations to clarify the meaning of the definition.In some cases, it may be necessary to define a term in relation to other concepts or phenomena. For example, if you were defining "gravity," you might explain how it relates to mass and distance. This can help to provide a more comprehensive understanding of the term.Defining a definition is an important skill that requires careful thought and clear communication. By following these guidelines, you can create definitions that are accurate, concise, and easily understood.。
RUNNING HEAD: IMPLICIT PROCESSESHow to define and examine implicit processes?Jan De Houwer and Agnes MoorsGhent University, Ghent, BelgiumIn Press. In R. Proctor & J. Capaldi, Implicit and Explicit Processes in the Psychology of Science.mailing address: Jan De HouwerPsychologyofDepartmentUniversityGhent2DunantlaanHenriGhentB-9000BelgiumJan.DeHouwer@UGent.beemail:phone: 0032 9 264 64 45fax: 0032 9 264 64 89AbstractResearch on implicit processes has become increasingly popular during the past two decades. Nevertheless, relatively little attention has been given to clarifying the meaning of the concept implicit. We propose implicit processes are processes that possess features of automaticity. Because different automaticity features do not necessarily co-occur, we recommend specifying the automaticity features one has in mind when using the term implicit. We provide an overview of the definitions of the various automaticity features that are used in the cognitive decompositional approach of automaticity. Because it is difficult to diagnose the cognitive automaticity features of mental processes, we explore for the first time a functional decompositional approach to automaticity in which automaticity features are defined in non-mental terms and used to describe effects. We end by discussing the implications of our analysis for research on the role of implicit processes in scientific behavior.Psychology as a scientific discipline is directed at understanding the processes underlying behavior. During the past two decades, more and more psychologists have become interested in the impact that so-called implicit processes have on behavior. This evolution can be seen in the various sub-disciplines of psychology, including general psychology (e.g., Schacter, 1987), social psychology (e.g., Greenwald & Banaji, 1995), clinical psychology (e.g., Gemar, Segal, Sagrati, & Kennedy, 2001), and addiction research (e.g., Wiers & Stacy, 2006). In line with this evolution, the current book addresses the topic of implicit and explicit processes in one particular aspect of human behavior, namely scientific behavior.Despite the huge interest in implicit processes, relatively little attention has been given to the question of what it means to say that a process is implicit. In this chapter, we present an overview of the conceptual work that we have performed in this context over the past years (see De Houwer, 2006, in press; De Houwer & Moors, 2007, 2010; De Houwer, Teige-Mocigemba, Spruyt, & Moors, 2009a, 2009b; Moors & De Houwer, 2006a, 2006b, 2007; Moors, Spruyt, & De Houwer, 2010). The starting point of our analysis is the postulate that the meaning of the term implicit is identical to the meaning of the term automatic. As we will argue later on, defining implicit as automatic has the advantage that it encompasses many of the earlier, more informal definitions that have been given for the concept implicit. It also allows one to draw on the extensive conceptual work on automaticity that has been undertaken in the past.In this chapter, we put forward the following ideas. First, automaticity is not an all-or-none property of mental processes but refers to a set of features that do not necessarily co-occur within each automatic process. Hence, it does not make sense to simply say that a process is implicit because there is no agreement about the automaticity features to which thisterm refers (see Bargh, 1989, 1992; Moors & De Houwer, 2006a, 2007). Second, the heterogenic nature of the concept automaticity calls for a decompositional approach in which researchers need to specify for each process which automaticity features are thought to apply to that process. This requires a precise definition of each automaticity feature. Within a cognitive decompositional approach (Bargh, 1992; Moors & De Houwer, 2006a, 2007), most automaticity features are defined in terms of mental constructs such as goals, consciousness, and processing resources. Hence, when using the term implicit, one should specify in a precise manner the automaticity features one has in mind (i.e., De Houwer, 2006; De Houwer et al., 2009a). Third, claims about automaticity features need to be backed up with sound arguments and empirical evidence. Hence, when claiming that a process is implicit in a certain manner, one should be able to justify these claims (De Houwer et al., 2009a, 2009b). Fourth, obtaining evidence for the automaticity of mental processes is complex (see Moors et al., 2010). Hence, studying the implicit nature of mental processes can run into important problems. Finally, we introduce the idea that progress in understanding the nature of implicit processes can be facilitated by a functional decompositional approach to automaticity (De Houwer, in press). This approach entails that automaticity features are defined strictly in terms of observable elements in the environment rather than unobservable mental constructs. In addition, the primary aim is to examine the automaticity features of effects (i.e., the causal impact of elements in the environment on behavior). Knowledge about the automaticity of effect, however, can be used to guide the construction of models about the mental processes by which elements in the environment influence behavior (see De Houwer, in press). We end the paper by illustrating the implications of our analysis for future research on implicit processes underlying scientific behavior.Automaticity is not an all-or-none featureAccording to the dual-mode or all-or-none view on automaticity, processes are either automatic or non-automatic. Automatic processes are assumed to have all automaticity features; non-automatic processes have the opposite features. For instance, all automatic processes are assumed to be unintentional, uncontrolled, unconscious, efficient, and fast whereas all non-automatic processes are assumed to be intentional, controlled, conscious, inefficient, and slow. According to this view, it is relatively easy to diagnose a process as automatic. It suffices to demonstrate that the process possesses one of the automaticity features. If it has one of the features, it can be assumed to have all other automaticity feature and thus to be fully automatic.It became clear, however, that the different automaticity features do not always co-occur. Evidence from Stroop studies, for instance, suggests that the processing of word meaning is automatic in that it does not depend on the intention to process the meaning of the word. At the same time, word processing is non-automatic in that it depends on the allocation of attention to the word (see Bargh, 1989, 1992, 1994, and Logan, 1985, 1989, for a discussion of this evidence). Several reasons can be identified for why the all-or-none view on automaticity remained popular despite the evidence against it (see Moors & De Houwer, 2007, for a discussion). Nevertheless, there can be little doubt about the fact that this view is incorrect.Given the assumption that the concepts automatic and implicit can be used in an interchangeable manner, one can conclude that it makes little sense to simply say that a process is implicit. This means little more than saying that a process possesses one or more features of automaticity, leaving it unspecified which automaticity features apply. Hence,when using the term implicit, it is best to always make explicit the automaticity features one refers. Such an approach can be called decompositional in that the concepts implicit and automatic are actually decomposed into various non-overlapping features. However, a decompositional approach is meaningful only if one can clearly define the different automaticity features in a non-overlapping way. In the next section, we discuss the cognitive decompositional approach that does incorporate detailed definitions of these features.The cognitive decompositional approach to automaticityWithin this section, we first provide an overview of the definitions of automaticity features as put forward by Moors and De Houwer (2006a, 2007). The main purpose of this overview is to show that detailed, non-overlapping definitions of automaticity features can and have been formulated. Researchers who want to make explicit their ideas about the implicit nature of processes can thus draw upon these definitions. Next we discuss whether the cognitive decompositional approach allows one to encompass the different definitions that have been given in the past for the concept implicit. Finally, we examine the problems that can arise when diagnosing whether a process possesses certain automaticity features. Defining automaticity featuresMany automaticity features such as (un)intentional, goal-directed, goal-(in)dependent, (un)controlled/(un)controllable, and autonomous, are somehow related to goals. Perhaps the most central goal-related automaticity feature is the feature uncontrolled. Uncontrolled is the opposite of controlled. We therefore start with a definition of controlled. To say that a process is controlled implies a proximal goal (i.e., a goal regarding the target process such as the goal to engage in, alter, stop, or avoid the process) that causes the achievement of the end state put forward in the goal (i.e., the actual occurrence, change, interruption, or preventionof the process). To say that a process is uncontrolled can therefore have different meanings. It can refer to the fact that the state of a process changes (i.e., that the process occurs, changes, is interrupted, or prevented) in the absence of a goal to achieve this change. It can also refer to the fact that the goal is present but the desired effect is absent. For instance, a process can be called uncontrolled when the process occurs despite the goal to prevent the occurrence of the process. A process can also be described as uncontrolled when both the goal and the desired effect are present, but the effect was not caused by the goal. Imagine that you have the goal to kneel down in front of your loved one in order to propose marriage. You do kneel down but it happens because someone pushes you. In that case, both the goal and the desired effect are present, but it would be wrong to regard the act of kneeling down as controlled because the goal did not cause the desired effect.Other goal-related automaticity features can be defined in terms of the feature uncontrolled. To say that a process is intentional means that the goal to engage in a process causes the occurrence of the process. Hence, intentional is identical to controlled in the sense of the goal to engage in (rather than the goal to alter, stop, or avoid the process). Therefore, unintentional processes are a subset of uncontrolled processes. A process can be termed unintentional because the process occurs without the intention to engage in the process or because the goal to engage does not cause the occurrence of the process. The feature autonomous can be defined as uncontrolled in terms of every possible processing goal. That is, an autonomous process is a process that is uncontrolled in every possible sense. Goal-independent processes are processes that operate independently from proximal goals (i.e., goals relating to the target process such as the goal to start, stop, alter, or avoid the process) and distal goals (i.e., goals unrelated to the target process). Therefore, a process might be uncontrolled but still be goal-dependent. Finally, processes can be described as purelystimulus-driven when in addition to being goal-independent (i.e., not dependent on any type of goal), they also do not depend on other factors such as awareness or attention. The occurrence of purely stimulus-driven processes depends only on the presence of a stimulus and certain basic conditions which ensure that the stimulus can be physically registered (e.g., in the case of visual stimuli, that the eyes are not closed).The feature unconscious is often listed as a core feature of automaticity. Unfortunately, (un)consciousness as a mental state is notoriously difficult to define (see Moors & De Houwer, 2006a, for an analysis). This limits its usefulness as a defining feature of automaticity. It is also important to realize that the term unconscious can be used as a predicate of several things. It can refer to (a) the stimulus input that evokes the process, (b) the output of the process, (c) the process itself, or (d) the consequences of the process such as its influence on subsequent processing (e.g., Bargh, 1994). Therefore, when using the term unconscious, it is important to specify what it is a predicate of. Note that there is no complete overlap between the feature (un)conscious and goal-related features. For instance, our definitions allow for the possibility of unconscious intentional processes, that is, a process that occurs only when there is a goal to engage in the process but that can operate in the absence of awareness of the process, its input, output, or consequences.A process can also be automatic in the sense of efficient. Efficient processes can be defined as processes that consume few processing resources or attentional capacity. Because efficiency leads to the subjective experience that processing is effortless, the terms efficient and effortless are often used interchangeably. Again there is no complete overlap between efficiency and other automaticity features. Whereas goals are related to the direction of attention (goals may determine the focus of attention), efficiency is related to the amount of attention. We also allow for the possibility of a state of consciousness that exists outside ofattention and thus for efficient conscious processes (Block, 1995). Likewise, in principle, processes can be unconscious but non-efficient (e.g., Naccache, Blandin, & Dehaene, 2002).Finally, processes can be automatic in the sense of fast. The feature fast refers to the time that is needed for a process to run to completion. This feature is clearly a gradual feature. There is no objective threshold for calling something fast or slow, so investigators need to rely on common sense arguments for calling some interval short or long or for deciding whether a process is fast or slow. Processes that are fast also tend to be uncontrolled (because the implementation of goals tends to require time), unaware (because consciousness needs time to develop), and efficient (because efficient processes are typically faster than non-efficient processes). Nevertheless, this overlap is only partial in that some uncontrolled, unconscious, or efficient processes might be slow and some controlled, conscious, or inefficient processes might be fast.Describing implicit processes in terms of automaticity featuresIn the past, researchers have described processes as being implicit because they operate in an unintentional, uncontrolled, efficient, fast, or unconscious manner. Our definition of implicit as automatic, in combination with a decompositional approach of automaticity allows us to encompass many if not all of the previous definitions of implicit processes. The decompositional approach also clarifies that researchers need to make explicit the automaticity feature that they have in mind when using the concept implicit and it provides them with the conceptual tools to precisely describe their definition of the crucial automaticity features. Hence, we hope that our analysis of the concepts implicit and automatic will improve communication amongst researchers when they study implicit processes.We do not commit ourselves to one particular automaticity feature as being the crucial feature for diagnosing whether a process is implicit. Others have put forward one specific feature such as the feature unintentional (e.g., Fazio & Olson, 2003; Richardson-Klavehn, Lee, Joubran, & Bjork, 1994) or the feature unconscious (e.g., Schacter, 1987) as the one feature that determines whether a process qualifies as implicit. In our opinion, the selection of a particular feature or set of features as central for implicit processes cannot be settled on the basis of a priori arguments but needs to be decided on the basis of empirical data. The distinction between implicit and explicit processes is functional only if it allow us to increase our understanding of (the processes underlying) behavior. If behavior is not differentially affected by whether the underlying process possesses a particular automaticity feature, then there is little added value in distinguishing between implicit and explicit processes on the basis of that automaticity feature. Hence, the concept implicit should be linked to the features that actually matter for behavior. For instance, research on implicit memory has shown that the conscious or unconscious nature of memory retrieval is relatively unrelated to the way in which variables influence memory retrieval (e.g., the impact of level of processing during encoding). What does seem to matter is whether retrieval is intentional (see Richardson-Klavehn et al., 1994). This suggests to us that it is more functional to define implicit memory in terms of the feature unintentional than in terms of the feature unconscious. It remains to be seen, however, whether these results generalize to processes other than memory retrieval. Until sufficient data are available for selecting one automaticity feature as central for the concept implicit, we prefer a broad definition of implicit as automatic covering several automaticity features. Of course, the selected features need to be made explicit and defined in an unambiguous manner.One could, however, argue that such a broad definition is problematic (e.g., Nosek &Greenwald, 2009). First, defining implicit as automatic and explicit as non-automatic might blur the distinction between implicit and explicit processes. We agree that most processes are probably automatic (and thus implicit) in some ways and non-automatic (and thus explicit) in other ways. This does indeed blur the distinction. However, at present we believe that defining implicit as automatic and explicit as non-automatic is the best available alternative. As indicated above, one could select one automaticity feature as the criterion that provides the dividing line, but it is not clear which criterion should be selected or whether it will ever be possible to reach consensus about the to-be-selected criterion. Hence, for now it is best not to commit to one feature but to adopt a broad definition that allows each researcher to make explicit the specific feature that they have in mind.Difficulties with examining the automaticity features of mental processesAlthough our definition of implicit does allow researchers to specify in great detail the conceptualization that they have in mind, even conceptually precise claims about the implicit nature of processes have little value when these claims cannot be backed up by valid arguments and empirical evidence. Therefore, claiming that a process is implicit not only requires precision but also justification. Unfortunately, several important challenges need to be overcome in order to obtain empirical evidence for automaticity features of mental processes. In this section, we provide a brief overview of these challenges (for more details, see De Houwer, in press; De Houwer, Gawronski, & Barnes-Holmes, 2010; Moors et al., 2010).Each automaticity feature refers to a certain set of conditions. For instance, the feature unintentional refers to a situation in which the goal to initiate a process is absent. A process issaid to possess a certain automaticity feature if it operates under the set of conditions specified by the automaticity feature. For instance, the process qualifies as unintentional if it operates in the absence of the goal to initiate that process. Therefore, in order to examine the automaticity features of a mental process, the following steps need to be taken: First, it needs to be established that the set of conditions that is specified by a feature, is actually present (e.g., that a person does not have the goal to initiate the process). Second, one should be able to ascertain that the process operates under those conditions.When adopting a cognitive decompositional approach, both steps are complicated by the fact that mental constructs cannot be observed directly (see De Houwer, in press; De Houwer et al., 2010). First, several automaticity features refer to the presence or absence of unobservable mental constructs. For instance, goal-related features refer to goals. Likewise, the feature efficient refers to the construct of mental resources. Goals and mental resources cannot be observed directly. One can at best infer their presence on the basis of observable elements in the environment or observable behavior (e.g., task instructions or performance on a secondary task) but such inferences depend on assumptions that might not always hold (e.g., that participants follow instructions or that a secondary task loads the same resources as the primary task).Second, many of the mental processes that are studied by cognitive psychologists (e.g., the formation of associations in memory, the activation of nodes and associations in semantic networks, working memory, and reasoning) cannot be observed directly. Consider the process of attitude activation (see Moors et al., 2010, for a more detailed discussion of this example). Attitudes can be conceived of as mental representations that specify a positive or negative disposition toward a certain object (e.g., Eagly & Chaiken, 2007). Unfortunately, attitudes (e.g., toward science) cannot be observed directly. Researchers have thereforedeveloped tasks that provide measures of attitudes. One of these measures is affective priming (Fazio, Sanbonmatsu, Powell, & Kardes, 1986; see De Houwer et al., 2009a, for a review). Affective priming refers to the observation that responses to a target stimulus (e.g., HAPPY) are faster when it is preceded by a prime stimulus with the same affective valence (e.g., SUMMER) than when the prime stimulus has a different valence (e.g., CANCER). Because affective priming can occur only if the attitude toward the prime stimulus has been activated, the observation of affective priming allows one to conclude that attitude activation has taken place. The automaticity of attitude activation can be examined by examining the conditions under which affective priming occurs. For instance, if affective priming occurs even when participants do not have the goal to retrieve their attitudes towards the prime stimuli, one can conclude that attitude activation was unintentional.There are, however, limitations to the extent to which the automaticity features of mental processes such as attitude activation can be inferred. First, although the presence of an observable effect (e.g., affective priming) allows one to infer the presence of certain mental processes (e.g., attitude activation), the absence or change in the size of the effect does not allow one to infer the absence of or change in the operation of a particular mental process. Affective priming, for instance, depends not only on the activation of the attitude towards the prime but also on other processes by which the activation of the prime attitude influences responses to the target (e.g., spreading of activation or response activation; see De Houwer et al., 2009a, for a review). Therefore, if affective priming is absent under the conditions specified by a particular automaticity feature (e.g., when participants do not direct attentional resources to the prime; e.g., Hermans, Crombez, & Eelen, 2000; but see Degner, 2009), this does not mean that attitude activation does not possess that automaticity feature (see Gawronski, Deutsch, LeBel, & Peters, 2008, and Moors et al., 2010, for a detailed discussionof this issue). More generally, because observable behavior is often (if not always) determined by multiple mental processes, it is difficult to draw strong conclusions about a single process based on the absence of or change in a particular behavior.A second limitation is that even the presence of a behavioral effect informs us only about mental processes that are conceptualized at the functional level. Marr (1982) pointed out that mental processes can be described at three levels: The functional, algorithmic, and implementational level. Functional descriptions of mental processes specify what the processes do, that is, which inputs are transformed into which outputs. For instance, at the functional level, attitude activation can be described as (a necessary part of) the mental process by which stimuli in the environment cause evaluative responses (see De Houwer et al., 2010). The algorithmic level deals with a symbolic description of the way in which the mental process achieves this function. For instance, attitude activation can be attributed to the activation of evaluative nodes in a semantic network (e.g., Fazio, 2007). Finally, the implementation level refers to how the mental process can be physically instantiated, for instance, in a (simulated) human brain.Importantly, there is a many-to-one relation between the algorithmic descriptions and functional descriptions. For instance, the functional process of attitude activation can be conceived of at the algorithmic level not only as involving the activation of evaluative nodes in a semantic network (e.g., Fazio, 2007) but also in terms of the retrieval of separate episodic memory traces (e.g., Schwarz, 2007). Therefore, although the presence of a behavioral effect (e.g., affective priming) allows for conclusions about the automaticity of functional mental processes (e.g., attitude activation), it does not allow for strong conclusions about the automaticity of algorithmic mental processes (e.g., activation of nodes in a semantic network). For instance, the conclusion that affective priming can occur in theabsence of the goal to process the prime does not allow for strong conclusions about whether evaluative nodes can be activated automatically in a semantic network (see Moors et al., 2010, for more details).SummaryThe cognitive decompositional approach has been elaborated in detail at the conceptual level (Moors & De Houwer, 2006a, 2007) and can help researchers to specify their conceptualization of the term implicit. Unfortunately, problems arise when studying the automaticity features of mental processes empirically. These problems result from the fact that automaticity features are defined in terms of mental constructs and the fact that researchers often aim to determine the automaticity features of (algorithmic) mental processes. In the next section, we therefore explore the possibility of developing a decompositional approach to automaticity that does not refer to mental constructs.Toward a functional decompositional approach to automaticity De Houwer (in press) recently proposed a functional-cognitive framework for psychological research that allows for a functional decompositional approach to automaticity. Within this approach, automaticity features are defined not in terms of mental constructs but in terms of observable elements of the environment. Although the functional decompositional approach is primarily directed at describing the automaticity of effects, it can also shed light on the nature of the mental processes that underlie behavior. We start this section by briefly explaining the functional-cognitive framework. Afterwards, we provide some preliminary ideas about how automaticity features might be defined in a functional, non-mental manner. Next, we explore for the first time how research on implicit processes might benefit from the functional decompositional approach to automaticity.。