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Record W2808952450 · doi:10.1177/1461444818781324

Women scholars’ experiences with online harassment and abuse: Self-protection, resistance, acceptance, and self-blame

2018· article· en· W2808952450 on OpenAlexafffund
George Veletsianos, Shandell Houlden, Jaigris Hodson, Chandell Gosse

Bibliographic record

VenueNew Media & Society · 2018
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsWestern UniversityMcMaster UniversityRoyal Roads University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHarassmentBlameScholarshipResistance (ecology)PsychologySocial psychologyCoping (psychology)Public relationsCriminologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Although scholars increasingly use online platforms for public, digital, and networked scholarship, the research examining their experiences of harassment and abuse online is scant. In this study, we interviewed 14 women scholars who experienced online harassment in order to understand how they coped with this phenomenon. We found that scholars engaged in reactive, anticipatory, preventive, and proactive coping strategies. In particular, scholars engaged in strategies aimed at self-protection and resistance, while often responding to harassment by acceptance and self-blame. These findings have important implications for practice and research, including practical recommendations for personal, institutional, and platform responses to harassment, as well as scholarly recommendations for future research into scholars’ experiences of harassment.

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)0.018
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0070.008
Scholarly communication0.0070.005
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.275
Teacher spread0.258 · 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

Citations169
Published2018
Admission routes2
Has abstractyes

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