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Record W4382562583 · doi:10.1037/amp0001149

Black sexual and gender diverse scholars' contributions to psychology.

2023· review· en· W4382562583 on OpenAlexaff
Jonathan Mathias Lassiter, Jeremy Garrett‐Walker, Kainaat Anwar, Ashley S Foye, Lourdes D. Follins

Bibliographic record

VenueAmerican Psychologist · 2023
Typereview
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsycINFOMainstreamRacismSociologyPsychologySnowball samplingGender studiesField (mathematics)CurriculumSocial psychologySocial sciencePedagogyPolitical scienceMEDLINELaw

Abstract

fetched live from OpenAlex

Psychology has a long history of perpetuating scientific racism and pathologizing gender and sexually diverse individuals. The field has been criticized for the reproduction of racism, sexism, cissexism, and other social inequities. This intersectional epistemological exclusion has led to a lack of appreciation for the work of Black sexual and gender diverse (SGD) scholars within the field of psychology. To highlight and center the contributions of Black SGD scholars in the field, we conducted an in-depth literature search of the work of 62 Black SGD scholars whose names and curricula vitae were obtained through email listservs, Twitter, and snowball sampling. In analyzing the work of the scholars, a total of 34 Black SGD scholars met inclusion criteria and had their research included in our review. We summarize their major contributions to the field of psychology in this article. Implications of these scholars' works and their potential to help address the lack of visibility of Black SGD scholars in mainstream psychology journals are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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)0.007
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0020.003
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.225
GPT teacher head0.559
Teacher spread0.334 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2023
Admission routes1
Has abstractyes

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