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Record W1993610694 · doi:10.1080/01973530802502309

Teaching About Racism: Pernicious Implications of the Standard Portrayal

2008· article· en· W1993610694 on OpenAlexaff
Glenn Adams, Vanessa A. Edkins, Dominika Lacka, Kate M. Pickett, Sapna Cheryan

Bibliographic record

VenueBasic and Applied Social Psychology · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRacismPrejudice (legal term)OppressionSociocultural evolutionPsychologyPhenomenonSocial psychologyPerceptionPsychometrics of racismRacial biasSociologyEpistemologyGender studiesLawPolitics

Abstract

fetched live from OpenAlex

Resonating with understandings prevalent among White Americans, psychologists tend to portray racism as a problem of individual prejudice rather than a systemically embedded phenomenon. An unintended consequence of this portrayal is to reproduce a narrow construction of racism as something that does not require energetic measures to combat. We describe 2 studies that provide support for this idea. Tutorials presented the topic of racism either as individual prejudice (standard condition) or as a systemic phenomenon embedded in American society (sociocultural condition). Results confirmed that perception of racism and (in Study 2) endorsement of antiracist policy were greater among participants in the sociocultural tutorial condition than among participants in the both the standard tutorial and no-tutorial control conditions. An ironic consequence of standard pedagogy may be to promote a modern form of scientific racism that understates the ongoing significance of racist oppression.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.007
Scholarly communication0.0030.004
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.365
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations106
Published2008
Admission routes1
Has abstractyes

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