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Record W2955645572 · doi:10.1177/0959353519855746

Reasonable men: Sexual harassment and norms of conduct in social psychology

2019· article· en· W2955645572 on OpenAlexafffund
Jacy L. Young, Peter Hegarty

Bibliographic record

VenueFeminism & Psychology · 2019
Typearticle
Languageen
FieldPsychology
TopicAcademic and Historical Perspectives in Psychology
Canadian institutionsQuest University Canada
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHarassmentPsychologyDilemmaSocial psychologyDisciplineReflexivitySociologyCriminologyGender studiesSocial scienceEpistemology

Abstract

fetched live from OpenAlex

Sexual harassment has received unprecedented attention in recent years. Within academia, it has a particularly reflexive relationship with the human sciences in which sexual harassment can be both an object of research and a problematic behavior amongst those engaged in that research. This paper offers a partial history in which these two are brought together as a common object of social psychology’s culture of sexual harassment. Here we follow Haraway in using culture to capture the sense-making that psychologists do through and to the side of their formal knowledge production practices. Our history is multi-sited and draws together (1) the use of sexual harassment as an experimental technique, (2) feminist activism and research which made sexual harassment an object of knowledge in social psychology, and (3) oral history accounts of sexual harassment amongst social psychologists. By reading these contexts against each other, we provide a thick description of how sexual harassment initiates women and men into cultures of control in experimental social psychology and highlight the ethical-epistemological dilemma inherent in disciplinary practices.

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)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.047
Scholarly communication0.0060.005
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.051
GPT teacher head0.418
Teacher spread0.367 · 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.

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

Citations35
Published2019
Admission routes2
Has abstractyes

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