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Record W4399205356 · doi:10.7202/1111374ar

L’intersectionnalité et l’histoire de la psychologie

2023· article· fr· W4399205356 on OpenAlexvenueno aff
Alexandra Rutherford, Tal Davidson, Rubis Le Coq, Marie Mathieu

Bibliographic record

VenueRecherches féministes · 2023
Typearticle
Languagefr
FieldPsychology
TopicPsychology Research and Bibliometrics
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

En tant que cadre conceptuel et analytique, l’intersectionnalité a influencé, et peut transformer, la façon dont les chercheur·ses abordent la psychologie et son histoire. L’intersectionnalité fournit un cadre pour examiner la manière dont de multiples catégories sociales se combinent dans des systèmes caractérisés à la fois par l’oppression et le privilège et affectent les expériences de celles et ceux qui occupent les intersections de ces catégories sociales. En outre, l’intersectionnalité a également été appliquée à l’écriture d’histoires de la psychologie qui prennent en considération le fonctionnement de plusieurs formes d’oppression et de privilège qui s’imbriquent. Par exemple, les historien·nes de la psychologie l’ont adoptée comme moyen d’aborder les intersections du racisme, du sexisme et de l’hétérocentrisme scientifiques dans l’histoire des concepts et des théories de la psychologie. L’intersectionnalité a aussi le potentiel de générer une compréhension historique plus sophistiquée du militantisme social des psychologues. Enfin, étant donné que les histoires existantes de la psychologie en contexte états-unien ont rendu largement invisible la contribution des femmes racisées, l’analyse intersectionnelle peut servir à rétablir et à mettre en avant leurs expériences et leur contribution.

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 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)0.014
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.172
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.014
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.009

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 head0.280
GPT teacher head0.507
Teacher spread0.227 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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