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Record W2553180385 · doi:10.1177/0886260516675922

Mother Blame and the Just World Theory in Child Sexual Abuse Cases

2016· article· en· W2553180385 on OpenAlexaff
Kelsi Toews, Jorden A. Cummings, Jessica L. Zagrodney

Bibliographic record

VenueJournal of Interpersonal Violence · 2016
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBlamePsychologySocial psychologyChild abuseSexual abuseChild sexual abusePoison controlSuicide preventionDevelopmental psychologyCriminologyMedicine

Abstract

fetched live from OpenAlex

Mothers are blamed for a variety of negative experiences and outcomes of their children, including child sexual abuse (CSA). According to just world hypothesis (JWH), people have a need to view the world as one where there is no such thing as an innocent victim; that is, the world is fair and just. These beliefs predict victim blaming in situations such as sexual abuse, physical abuse, and robbery. However, JWH has not been applied to the examination of mother blame, a situation in which the blame target did not directly experience the traumatic event. We examined this application in two studies: (a) a thematic analysis of focus group discussions and (b) a correlational study. Across both studies, participants identified personal characteristics of the mother that either increased or decreased blame, consistent with JWH. However, when directly asked, most participants denied holding just world beliefs, particularly as related to child sexual abuse. Our results indicate that methodological choices might affect results, and that socially constructed views of "ideal mothers" influence mother blame. We discuss implications for validity of just world theory (JWT), methodological choices, and reduction of mother blame.

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)0.013
metaresearch head score (Gemma)0.057
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.057
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.008
Scholarly communication0.0020.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.288
Teacher spread0.270 · 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

Citations16
Published2016
Admission routes1
Has abstractyes

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