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11.2 When Is Science Suspect: Difference between revisions

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{{Cover|11.2 When Is Science Suspect}}


The capacity for science to be misused to reinforce existing power structures.
As scientific studies inform societal views and policy, it is important to recognize how science, particularly when groups in power study groups out of power, has been misused to perpetuate injustice. With the help of historical examples, we aim to instill in students a sense of social responsibility as future scientists and decision makers.
 
{{Navbox}}


== The Lesson in Context ==
== The Lesson in Context ==
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<!-- Always begin section with a description of this lesson in relation to the course as a whole. -->
<!-- Always begin section with a description of this lesson in relation to the course as a whole. -->
After discussing how science may go wrong in its ''form'' in [[11.1 Pathological Science]], we now discuss how science may go wrong in its societal ''outcome''. Through a discussion activity, students will appreciate the difficulty in measuring humans in a way that is absolutely free of confounds that have discriminatory implications, and thus learn the importance of social responsibility as a scientist and including a more representative array of voices in selecting scientific questions and designing measures.
After discussing how science may go wrong in its ''form'' in [[11.1 Pathological Science]], we now discuss how science may go wrong in its societal ''outcome''. Through a discussion activity, students will appreciate the difficulty in measuring humans in a way that is absolutely free of confounds that have discriminatory implications, and thus learn the importance of social responsibility as a scientist and including a more representative array of voices in selecting scientific questions and designing measures.
 
<!-- Expandable section relating this lesson to other lessons. -->
<!-- Expandable section relating this lesson to earlier lessons. -->
{{Expand|Relation to Other Lessons|
{{Expand|Relation to Earlier Lessons|
'''Earlier Lessons'''
{{ContextLesson|1.1 Introduction and When Is Science Relevant}}
{{ContextLesson|1.1 Introduction and When Is Science Relevant}}
{{ContextRelation|If science is to be used in societal decision making, scientists must be mindful of its potential to cause harm.}}
{{ContextRelation|If science is to be used in societal decision making, scientists must be mindful of its potential to cause harm.}}
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{{ContextRelation|It is tempting, though improper, for scientists to only publish results in a way that confirms the predominant belief. When it comes to human groups, this belief may be a mere stereotype.}}
{{ContextRelation|It is tempting, though improper, for scientists to only publish results in a way that confirms the predominant belief. When it comes to human groups, this belief may be a mere stereotype.}}
}}
}}
== Takeaways ==
== Takeaways ==


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|-|Examples=
|-|Examples=


<!-- Example formatting is still experimental. -->
{{Example
'''The Flynn Effect'''
|The Flynn Effect
: Scores from IQ tests are typically normalized such as to set the average score to be 100. But, throughout the 20th century, there was a consistent increase in how well people performed on IQ tests. This means that when newer test subjects take older tests, the average scores tended to be greater than 100.
|Scores from IQ tests are typically normalized such as to set the average score to be 100. But, throughout the 20th century, there was a consistent increase in how well people performed on IQ tests. This means that when newer test subjects take older tests, the average scores tended to be greater than 100.}}
{{Line}}
{{Example
'''Cognitive Capabilities by Ethnicity and Gender'''
|Cognitive Capabilities by Ethnicity and Gender
: For a long time many scientists asserted that women and ethnic minorities were less intelligent than men of European descent, using studies of cognitive performance normalized to the skills such men spent their time developing. This claim was used to justify withholding educational and vocational opportunities for both women and minorities, as well as institutional and legal power.
|For a long time many scientists asserted that women and ethnic minorities were less intelligent than men of European descent, using studies of cognitive performance normalized to the skills such men spent their time developing. This claim was used to justify withholding educational and vocational opportunities for both women and minorities, as well as institutional and legal power.}}
{{Line}}
{{Example
'''Genetic Description of Pellagra'''
|Genetic Description of Pellagra
: In 1916, Charles Davenport asserted that the disease pellagra was genetic (racial). However, it is actually caused due to nutritional factors (a lack of vitamin B3).
|In 1916, Charles Davenport asserted that the disease pellagra was genetic (racial). However, it is actually caused due to nutritional factors (a lack of vitamin B3).
{{LinkCardInternal
|links={{LinkCardInternal
|url=:File:Davenport - 1916 - The Hereditary Factor In Pellagra.pdf
|url=:File:Davenport - 1916 - The Hereditary Factor In Pellagra.pdf
|title=The Hereditary Factor In Pellagra
|title=The Hereditary Factor In Pellagra
|description=The introduction from Davenport's book describing his racial theory of pellagra.}}
|description=The introduction from Davenport's book describing his racial theory of pellagra.}}
{{Line}}
}}
'''Representation and External Validity'''
{{Example
: Historically, psychological or medical studies are often performed on predominantly white, wealthy, young males, with conclusions that are nevertheless generalized to the culturally and genetically diverse global population. For example, while blacks and Latinos make up 30% of the US population, they represent only 6% of participants in federally funded studies. While the studies themselves may not be discriminatory explicitly or implicitly, the differing validity of their application to diverse populations may be a source of furthered inequality.
|Representation and External Validity
{{LinkCardInternal
|Historically, psychological or medical studies are often performed on predominantly white, wealthy, young males, with conclusions that are nevertheless generalized to the culturally and genetically diverse global population. For example, while blacks and Latinos make up 30% of the US population, they represent only 6% of participants in federally funded studies. While the studies themselves may not be discriminatory explicitly or implicitly, the differing validity of their application to diverse populations may be a source of furthered inequality.
|links={{LinkCardInternal
|url=:File:Oh et al. - 2015 - Diversity in Clinical and Biomedical Research A P.pdf
|url=:File:Oh et al. - 2015 - Diversity in Clinical and Biomedical Research A P.pdf
|title=Diversity in Clinical and Biomedical Research: A Promise Yet to Be Fulfilled
|title=Diversity in Clinical and Biomedical Research: A Promise Yet to Be Fulfilled
|description=A paper on this phenomenon.}}
|description=A paper on this phenomenon.}}
<br />
}}
{{Exemplary
|{{Blockquote|The data described in ''The Bell Curve'' shows that Black students perform more poorly on IQ tests than White students. But historically, tests like that have been used to justify existing power structures and racial oppression, so maybe we should think about that more carefully before we interpret it to mean that White students are smarter. The IQ tests were written by Whites, for students who had grown up in similar environments. Maybe there are cultural biases. And hang on, there's a lot of vocabulary on those tests; that requires education, and we know that there are systemic racial inequalities in the education system. That by itself could explain the difference.}}
}}


|-|Common Misconceptions=
|-|Common Misconceptions=
Line 75: Line 76:
{{Misconception|Only a racist scientist could produce a racially discriminatory study.|As an example, a perfectly well-intentioned scientist studying the level of altruism in people across ethnic groups may come up with an operational definition of altruism that is based on their own cultural understanding of the term and fails to include alternative expressions of altruism in other cultures.|first=yes}}
{{Misconception|Only a racist scientist could produce a racially discriminatory study.|As an example, a perfectly well-intentioned scientist studying the level of altruism in people across ethnic groups may come up with an operational definition of altruism that is based on their own cultural understanding of the term and fails to include alternative expressions of altruism in other cultures.|first=yes}}


</tabber>
|-|Expanded Learning Goals=


== Useful Resources ==
After this lesson, students should
 
# Attitudes
<tabber>
## Recognize the potential to abuse science for social and political ends.
 
## Show heightened caution in situations in which science involves the study of human groups and subsequent validation of societal power structures.
|-|Lecture Video=
## Recognize that you are yourself always implicated in some social dynamic or other that may be relevant to the assessment of any particular study of human groups.
 
# Concept Acquisition
<br /><center><youtube>9pcntu0WNBE</youtube></center><br />
## Historically, science has been abused for social and political ends; this is why we should show heightened caution (using all the tools of the course) whenever science is used to evaluate particular groups of people.
 
## The abuse of science for social and political ends is exacerbated by confirmation bias and badging.
|-|Discussion Slides=
## The abuse of science for social and political ends is particularly insidious when it involves the study of human groups and subsequent validation of societal power structures (typically by purporting to establish that some group(s) have lesser capacities than others).
 
## '''Just World Fallacy:''' The tendency to believe that outcomes are deserved and existing social structures are justified.
{{LinkCard
# Concept Application
|url=https://docs.google.com/presentation/d/1hL0N_VUUlQnjbDAfC1DmZkZseRShTYsYYqc6ws_Ijsc/
## Identify examples in which there exists the potential for abuse of science for social and political ends.
|title=Discussion Slides Template
## Explain how confirmation biases and badging can exacerbate the abuse of science for social and political ends in particular cases.
|description=The discussion slides for this lesson.
## Explain why the potential to abuse science for social and political ends is particularly insidious when it involves the study of human groups and subsequent validation of societal power structures, using a real example.
}}
## Explain how the validity of human classifications (e.g., race, gender) can be problematic and contribute to such abuses (e.g., because there isn't any there, there are a number of things there, there is one thing there but it doesn't have the significance you take it to).
<br />
## Provide historical examples of the abuse of science for social and political ends.
 
## Provide examples of cases in which the study of human groups is legitimate and beneficial.
|-|Readings and Assignments=
## Distinguish legitimate inquiry into human groups from bad science.
 
{{LinkCardInternal
|url=:File:The Mismeasure of Man Introduction - Gould.pdf
|title=The Mismeasure of Man
|description=The introduction from Stephen Jay Gould's book.}}
<br />


</tabber>
</tabber>


== Recommended Outline ==
{{#restricted:{{Private:11.2 When Is Science Suspect}}}}
 
{{NavCard|chapter=Lesson plans|text=All lesson plans|prev=11.1 Pathological Science|next=12.1 Wisdom of Crowds and Herd Thinking}}
=== Before Class ===
 
Carefully review the [[#Research Agendas|case studies]] so that you're ready to answer any questions on them during the lesson.
 
=== During Class ===
 
{| class="wikitable" style="margin-left: 0px; margin-right: auto;"
|10 Minutes
|Go through the [[#Discussion Questions|discussion questions]].
|-
|35 Minutes
|Do the [[#Admissions Officer|admissions officer]] activity.
|-
|5 Minutes
|Go through the [[#Measuring Happiness|happiness]] discussion.
|-
|10 Minutes
|Present the [[#Vocabulary Tests|vocabulary tests]] example.
|-
|10 Minutes
|Present several [[#Research Agendas|case studies]] of inclusionary research agendas.
|-
|10 Minutes
|Run the [[#Closing Discussion|closing discussion]].
|}
 
== Lesson Content ==
 
=== Discussion Questions ===
 
Break up into small groups for this activity.
 
# What groups are you a part of? Academic, social, cultural, interest-based groups? Are you a part of multiple groups? What groups do you have in common?
#* What is your most salient (noticeable, important) group in the eyes of other people?
#* What about in your own eyes?
# Why do we "talk up" and think better of groups we are in? What experiences have you had thinking better of a group (e.g. your high school sports team, home room, or grade cohort) because you were a part of it?
# Should we give up altogether on the study of human groups, or is there important work to be done if we are careful?
{{BoxCaution|Note that we are ''not'' just talking about ethnic groups.}}
=== Admissions Officer ===
 
We want to demonstrate how difficult it is to come up with criteria with which to measure humans without inadvertently disadvantaging certain groups of people. Nevertheless, these imperfect criteria are often better able to select for certain traits than picking people at random. Having ''some'' form of criteria is often necessary. In this activity the students are split into small groups and have to act as admissions officers for various groups and teams. The tests are meant to capture the most important aspects of one's aptitude in a certain task, but due to limited time and resources, these tests are inevitably highly flawed.
 
==== Instructions ====
 
{| class="wikitable" style="margin-left: 0px; margin-right: auto;"
|6 Minutes
|[[#Explanation|Explain]] the activity.
|-
|7 Minutes
|The students discuss and write their criteria down.
|-
|7 Minutes
|The students present their criteria to a paired group and listen to the other group's criteria.
|-
|7 Minutes
|The students brainstorm why the other group's specific criteria may inadvertently exclude kids that are still really good "players" if judged holistically but wouldn't score well on those specific criteria.
|-
|4 Minutes
|Have your class go over the [[#Admissions Discussion Questions|admissions discussion questions]].
|-
|4 Minutes
|Have your class discuss the activity's relevance to science with the [[#Reflection on AOT|AOT questions]].
|}
 
==== Explanation ====
 
Students will work in small groups. As an admissions officer, they need to select 20 out of 300 kids. You only have one day of "tryouts" or "test", for 5 minutes per applicant. You can give them any test/task you want, but at the end of the day, the choices must depend only on the result of this test. There can be many test items/questions/tasks, but each applicant needs to receive one numerical value at the end of their 5-min test.
{{BoxTip|title=Reminder|The 20 applicants with the highest scores are admitted. This test is the '''only''' criterion that will be used to judge the applicants.}}
Tell your students to do their best to select people who have the highest aptitude in the given prompt. Clearly write down the task and criteria for judging.
 
==== Prompts ====
 
# American football team
# Chess (note a full game typically takes 10 min)
# Children's choir
# Honours mathematics programme
# Programming camp
# Rock climbing
# Rowing
# E-sports league (e.g. Smash bros)
# Model UN
# History camp
# Poetry group
 
==== Admissions Discussion Questions ====
 
# What are the difficulties that you faced when coming up with selection criteria?
# Why might an otherwise highly qualified kid be rejected?
# How would you improve your criteria or the selection process itself to be more inclusive?
 
==== Reflection on AOT ====
 
"My blood boils over whenever a person stubbornly refuses to admit he's wrong." This question is from the AOT test from [[3.2 Calibration of Credence Levels]].
 
# What should we be mindful of when we do and interpret this study, especially for comparison between human groups?
# Should we not have given you the AOT test?
 
==== Takeaways ====
 
* Designing ''valid'' criteria to measure humans is difficult. Often, certain groups are disadvantaged in these measurements.
* Nevertheless, even imperfect criteria have advantages over instinct, nepotism, randomness... ''Some'' sort of criteria often seems necessary.
* We can accept that a test will be highly flawed, while still striving for a ''valid and equitable'' test. We can do our best on this impossible (but necessary) task.
 
=== Measuring Happiness ===
 
This discussion brings up a metric that people often ''do'' want to study in the real world and the cross-cultural difficulties inherent in doing so.
 
==== Initial Happiness Discussion Questions ====
 
Students should discuss the following questions in small groups.
# In your group, what are all the languages that you collectively speak or understand?
# What are all the words for "happiness" in these languages?
#* Do they mean the same thing?
#* What are the nuanced differences between the meanings of these different words for "happiness?"
# If you wanted to compare the levels of happiness among speakers of different languages, how might the different words affect the results of these studies?
[[File:Happiness Table.png|thumb|right|Uchida & Ogihara, 2012]]Then show the students the table. It was put together by the Japanese researchers Uchida and Ogihara to emphasize the cross-cultural differences in meanings of happiness that make it challenging to compare across different countries. Afterwards, ask the students to discuss the following prompt.
* Suppose you are in charge of an international scientific funding institution and want to promote projects that measure and compare "happiness" across different languages, contexts, and cultures.
* You want the studies to be as ''reliable'' and ''valid'' as possible.
* What conditions would you put in place or look for in the research you fund to support this?
{{BoxAnswer|There isn't any ''one'' answer to this question. But, we're hoping the students come up with the idea of needing researchers from a diverse set of backgrounds from the very beginning of study design.}}
=== Vocabulary Tests ===
 
[[File:PPVT Example.png|thumb|right|Some example images from the Peabody Picture Vocabulary Test.]] This is a presentation of preliminary research from a former SSS instructor that demonstrates how even metrics which ''seem'' culturally impartial might surprise us in how they don't generalize.
 
Cross-cultural vocabulary tests like the Peabody Picture Vocabulary Test (PPVT) uses simple images and has young kids identify them when given prompts in their native language. The test is supposed to be able to be applied universally. But, it turns out that there are contexts like rural (and semi-rural) Kenya where kids may have limited exposure to picture books and other iconography. [[File:Picture Book Word Learning Plot.png|thumb|right|Results for toddlers that do and do not look regularly at picture books.]]
 
[https://www.youtube.com/watch?v=bovdFze3MN0&t=9s Preliminary research] suggests that these kids without as much exposure to images perform worse on picture-based vocabulary tests.
 
[[File:Proportion Correct for Different Types of Vocabulary.png|thumb|right|When given actual instead of images, toddlers perform better.]] However, this ''doesn't'' mean that the kids actually have worse vocabularies. When given actual objects instead of images of them, the kids perform substantially better.
{{BoxCaution|The objects were all selected to be ones that the toddlers would encounter in everyday life. The images were drawn to match the PPVT style but be otherwise identical to the 3D objects.}}
==== Details About Study 1 ====
 
* 32 toddler participants (mean = 2.29 years, SD = 0.21 years, 2.01 - 2.87 years)
* 5 additional toddlers tested (4 fussouts, 1 experimenter error)
* Tests were performed by local experimenter that speaks Luo, the first languages of the kids.
* Study was done following best research practices for cross-cultural psychology.
*: Local collaborators, community involvement, timely dissemination of results to participating schools/parents, etc.
 
==== Details About Study 2 ====
 
* Preregistered sample of n = 192 (96 children per condition)
** All children in their first year of formal schooling
** Within sample, lots of diversity in picture experience
** M = 4.56 years, SD = .96 years, range = 2.38 - 7.48 years
* Gave the kids PPVT-style vocabulary assessment in Swahili, involving either pictures or objects
* For half the kids give them pictures, for the other half gave them objects
* Note that figure includes the combined results for nouns, numbers, and colors
** The kids do very well with nouns overall
** The kids have some trouble with numbers - this is common across cultural contexts
** The kids really have difficulty with colors. They rarely perform better than chance. This is because colors are not something normally taught in this part of Kenya. And, when it is taught, its only much later, in schools, and in English. Many of the (adult) local collaborators also had difficulty identifying colors in their otherwise native language.
* Note that this test was done in Mombasa.
** Mombasa is the second-largest city in Kenya and is a much more urban environment.
** That being said, this was done in one of the more rural parts of Mombasa.
** Almost all the schools in this area have murals on their walls. So, theres definitely some exposure to iconography.
 
=== Research Agendas ===
 
This section is a series of case studies of different researchers from historically underrepresented backgrounds. Their research direction was shaped by their background and, in all cases, wasn't a priority for other researchers in the field. We hope to address the following points.
* Having researchers from diverse backgrounds can affect the ''quality'' of cross-cultural science being done.
* How can it also affect which scientific questions are studied in the first place?
* What are the boundary conditions for generalization? If results replicate in two cultures? Every single culture?
* If most populations respond one way and there is one exception, what kind of conclusions can be drawn?
 
==== Case Study 1 ====
 
[[File:Flores Image 1.png|thumb|right|Africa Flores]]
{{LinkCardInternal
|url=:File:Africa Flores Reading.pdf
|title=Africa Flores
|description=More information on Africa Flores.}}
{{BoxCaution|This document was made for a different iteration of the course. Some of the discussion in it may not be relevant.}}
* The rivers near Africa Flores' village in Guatemala were all too polluted from sewage and agricultural chemicals to be potable.
* However, she discovered the water in other parts of the country was clean.
* Ultimately discovered that the discrepancy was due in part to a lack of information about water quality in different areas.
* Data on Guatemalan water quality was already being taken by the U.S. Geological Survey (USGS) and NASA since 1970.
* In 2008, that data was finally open sourced.
* Flores took ground samples to calibrate the satellite data and track changes in the 2009 Lake Atitlan toxic algal bloom.
* She works for NASA to track environmental changes in data-poor countries.
 
==== Case Study 2 ====
 
[[File:Itchuaqiyaq Image.png|thumb|right|Cana Uluak Itchuaqiyaq]]
{{LinkCard
|url=https://www.itchuaqiyaq.com/about-me
|title=Cana Uluak
|description=More information on Cana Uluak Itchuaqiyaq (including name pronunciation).}}
* Native Alaskans have been historically excluded from academia and academic texts.
* Combines her Arctic experiences and Inuit perspective with theory and data to develop effective methods that equip others in planning and conducting respectful research, teaching, organizational strategy, and advocacy work.
* Assistant professor of professional and technical writing at Virginia Tech who focuses on empowerment, social justice, and equitable research practices.
 
==== Case Study 3 ====
 
* Kaposi's Sarcoma-Associated Herpesvirus causes cancer in immunocompromised individuals (usu. HIV/AIDS patients).
* Originally characterized in part by researchers at UCSF after the HIV pandemic.
* Now primarily endemic in Africa where HIV positivity rates are high.
* Schism between what is studied in the US and other Western countries (basic biology) versus Africa-based groups pursuing epidemiology and clinically-focused research.
* Formerly, the annual KSHV conference was always in the US. Now to promote better collaboration and accessibility, the conference is in South Africa every other year.
 
=== Closing Discussion ===
 
What should scientists be mindful of when studying human groups or members of a different group?<!-- == Overflow ==
 
<div class="toccolours mw-collapsible mw-collapsed" style="overflow:auto;">
<div style="font-weight:bold;line-height:1.6;">Extra content that's not currently part of the official lesson plan.</div>
<div class="mw-collapsible-content">
 
=== Relevant Articles ===
 
Some of these are examples. Some of these are deep dives into related problems. Most lean heavily on the "mismeasure of man" part of this section. All need work to figure out how to integrate into the lesson. {{Todo|Figure everything out.}}
 
* [https://nautil.us/is-there-any-place-for-race-in-medicine-293223/ Is There Any Place for Race in Medicine?]
* [https://undark.org/2023/01/25/criminologists-looking-to-biology-for-insight-stir-a-racist-past/ Criminologists, Looking to Biology for Insight, Stir a Racist Past]
 
</div></div> -->{{NavCard|prev=11.1 Pathological Science|next=12.1 Wisdom of Crowds and Herd Thinking}}
[[Category:Lesson plans]]
[[Category:Lesson plans]]

Latest revision as of 23:48, 11 June 2026

As scientific studies inform societal views and policy, it is important to recognize how science, particularly when groups in power study groups out of power, has been misused to perpetuate injustice. With the help of historical examples, we aim to instill in students a sense of social responsibility as future scientists and decision makers.

The Lesson in Context

After discussing how science may go wrong in its form in 11.1 Pathological Science, we now discuss how science may go wrong in its societal outcome. Through a discussion activity, students will appreciate the difficulty in measuring humans in a way that is absolutely free of confounds that have discriminatory implications, and thus learn the importance of social responsibility as a scientist and including a more representative array of voices in selecting scientific questions and designing measures.

Earlier Lessons

1.1 Introduction and When Is Science Relevant
  • If science is to be used in societal decision making, scientists must be mindful of its potential to cause harm.
1.2 Shared Reality and Modeling
  • When studying humans, it is often necessary to define terms operationally, such as "what behaviors constitute altruism." Such operationalizations may be a crude measure of a more realist human trait, if one even exists. Cultural and personal biases may easily cause such definitions to favor the researchers' own group at the expense of others.
10.1 Confirmation Bias
  • It is tempting, though improper, for scientists to only publish results in a way that confirms the predominant belief. When it comes to human groups, this belief may be a mere stereotype.

Takeaways

After this lesson, students should

  1. Recognize the potential to abuse science for social and political ends.
  2. Show heightened caution in situations in which science involves the study of human groups and subsequent validation of societal power structures.
  3. Recognize that you yourself are always involved in some social dynamic that may be relevant to the assessment of any particular study of human groups.

While it may be easy to spot cases where science has been used intentionally to validate preexisting societal power structures, the goal of this lesson is to bring attention to the possibility that one may inadvertently use science in such a way, through negligence or even with the best intentions, especially when it comes to the study of human groups.


Just World Fallacy

The tendency to believe that outcomes are deserved and existing social structures are justified.

Reliability

The extent to which some metric is consistently measurable.

Validity

The extent to which some metric or scientific concept reflects some real external thing.

External Validity

The extent to which the result of an RCT generalizes to the world at large. There are several reasons results might not generalize.
  • Longevity of Effect
Lab studies tend to measure dependent variables immediately after the intervention, but often inferences are desired for long-term effects.
  • Disruption Effect
Awareness of being in an experiment.
  • Novelty Effect
Novelty of the context may alter the effects of the experiment.


The Flynn Effect

Scores from IQ tests are typically normalized such as to set the average score to be 100. But, throughout the 20th century, there was a consistent increase in how well people performed on IQ tests. This means that when newer test subjects take older tests, the average scores tended to be greater than 100.

Cognitive Capabilities by Ethnicity and Gender

For a long time many scientists asserted that women and ethnic minorities were less intelligent than men of European descent, using studies of cognitive performance normalized to the skills such men spent their time developing. This claim was used to justify withholding educational and vocational opportunities for both women and minorities, as well as institutional and legal power.

Genetic Description of Pellagra

In 1916, Charles Davenport asserted that the disease pellagra was genetic (racial). However, it is actually caused due to nutritional factors (a lack of vitamin B3).

Representation and External Validity

Historically, psychological or medical studies are often performed on predominantly white, wealthy, young males, with conclusions that are nevertheless generalized to the culturally and genetically diverse global population. For example, while blacks and Latinos make up 30% of the US population, they represent only 6% of participants in federally funded studies. While the studies themselves may not be discriminatory explicitly or implicitly, the differing validity of their application to diverse populations may be a source of furthered inequality.

Exemplary Quotes

The data described in The Bell Curve shows that Black students perform more poorly on IQ tests than White students. But historically, tests like that have been used to justify existing power structures and racial oppression, so maybe we should think about that more carefully before we interpret it to mean that White students are smarter. The IQ tests were written by Whites, for students who had grown up in similar environments. Maybe there are cultural biases. And hang on, there's a lot of vocabulary on those tests; that requires education, and we know that there are systemic racial inequalities in the education system. That by itself could explain the difference.

Only a racist scientist could produce a racially discriminatory study.

As an example, a perfectly well-intentioned scientist studying the level of altruism in people across ethnic groups may come up with an operational definition of altruism that is based on their own cultural understanding of the term and fails to include alternative expressions of altruism in other cultures.

After this lesson, students should

  1. Attitudes
    1. Recognize the potential to abuse science for social and political ends.
    2. Show heightened caution in situations in which science involves the study of human groups and subsequent validation of societal power structures.
    3. Recognize that you are yourself always implicated in some social dynamic or other that may be relevant to the assessment of any particular study of human groups.
  2. Concept Acquisition
    1. Historically, science has been abused for social and political ends; this is why we should show heightened caution (using all the tools of the course) whenever science is used to evaluate particular groups of people.
    2. The abuse of science for social and political ends is exacerbated by confirmation bias and badging.
    3. The abuse of science for social and political ends is particularly insidious when it involves the study of human groups and subsequent validation of societal power structures (typically by purporting to establish that some group(s) have lesser capacities than others).
    4. Just World Fallacy: The tendency to believe that outcomes are deserved and existing social structures are justified.
  3. Concept Application
    1. Identify examples in which there exists the potential for abuse of science for social and political ends.
    2. Explain how confirmation biases and badging can exacerbate the abuse of science for social and political ends in particular cases.
    3. Explain why the potential to abuse science for social and political ends is particularly insidious when it involves the study of human groups and subsequent validation of societal power structures, using a real example.
    4. Explain how the validity of human classifications (e.g., race, gender) can be problematic and contribute to such abuses (e.g., because there isn't any there, there are a number of things there, there is one thing there but it doesn't have the significance you take it to).
    5. Provide historical examples of the abuse of science for social and political ends.
    6. Provide examples of cases in which the study of human groups is legitimate and beneficial.
    7. Distinguish legitimate inquiry into human groups from bad science.

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