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Record W3095973620 · doi:10.15195/v7.a22

Microaggressions in the United States

2020· article· en· W3095973620 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSociological Science · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsnot available
FundersYork University
KeywordsRacismOppressionPsychologyMental healthSocial psychologyPopulationAggressionGeneral Social SurveyGender studiesSociologyDemographyPolitical sciencePsychiatryPolitics

Abstract

fetched live from OpenAlex

'Microaggressions' is the term scholars and cultural commentators use to describe the ways that racism and other systems of oppression are upheld in everyday interactions. Although prior research has documented the types of microaggressions that individuals experience, we have lacked representative data on the prevalence of microaggressions in the general population. We introduce and evaluate five new survey items from the 2018 General Social Survey intended to capture five types of microaggressions. We assess the prevalence of each microaggression as well as a constructed microaggression scale across a key set of sociodemographic characteristics. We find that black Americans experience more microaggressions than other racialized groups, twice the rate of the general public for some types. Younger people report more microaggressions than older people. Women are more likely to report some types of microaggressions, and men others. Experiencing microaggressions is associated with an array of negative physical and mental health outcomes.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.208
GPT teacher head0.459
Teacher spread0.251 · 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