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Record W2143317770 · doi:10.1177/088626000015003002

Minimizing Negative Experiences

2000· article· en· W2143317770 on OpenAlexaff
Katherine Dunham, Charlene Y. Senn

Bibliographic record

VenueJournal of Interpersonal Violence · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPhenomenonLogistic regressionPsychologyHuman factors and ergonomicsPoison controlSocial psychologyClinical psychologyMedicineMedical emergency

Abstract

fetched live from OpenAlex

Women who have experienced abuse in intimate relationships often omit information about the abuse when disclosing to others. Data describing this phenomenon have been anecdotal and concerned only with disclosures to clinicians and social scientists. This study documented the prevalence of minimization in disclosures to friends and relatives and explored factors that might predict minimization. The results revealed that 36.1% of women who disclosed abuse to friends and relatives omitted information. A stepwise logistic regression indicated increased severity of abuse, more accepting attitudes toward physical abuse, and delayed disclosure were each positively associated with minimization. We tentatively suggest that this phenomenon can be viewed as an attempt to manage confidants' reactions to disclosure of abuse and enhance the likelihood of social support. Whether providing an incomplete picture of the situation serves to facilitate or undermine the quality of social support received is an empirical question that must be explored.

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)0.002
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
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 head0.020
GPT teacher head0.323
Teacher spread0.303 · 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 designObservational
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

Citations78
Published2000
Admission routes1
Has abstractyes

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