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Record W2161879432 · doi:10.1177/0886260507304550

Predictors of Adult Attitudes Toward Corporal Punishment of Children

2007· article· en· W2161879432 on OpenAlexaffabout
Marie‐Hélène Gagné, Marc Tourigny, Jacques Joly, Joëlle Pouliot-Lapointe

Bibliographic record

VenueJournal of Interpersonal Violence · 2007
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversité de SherbrookeUniversité Laval
Fundersnot available
KeywordsSpankingCorporal punishmentChild disciplinePoison controlInjury preventionPsychologyChild abuseSuicide preventionHuman factors and ergonomicsPhysical abuseDomestic violenceHarmOccupational safety and healthClinical psychologyDevelopmental psychologyMedicineSocial psychologyMedical emergency

Abstract

fetched live from OpenAlex

This study identifies predictors of favorable attitudes toward spanking. Analyses were performed with survey data collected from a representative sample of 1,000 adults from Quebec, Canada. According to this survey, a majority of respondents endorsed spanking, despite their recognition of potential harm associated with corporal punishment (CP) of children. The prediction model of attitudes toward spanking included demographics, experiencing or witnessing various forms of family violence and abuse in childhood, and perceived frequency of physical injuries resulting from CP. Spanking was the most reported childhood experience (66.4%), and most violence and abuse predictors were significantly and positively correlated. Older respondents who were spanked in childhood and who believed that spanking never or seldom results in physical injuries were the most in favor of spanking. On the other hand, respondents who reported more severe physical violence or psychological abuse in childhood were less in favor of spanking. Findings are discussed in terms of prevention of CP and family coercion cycle.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.141
Threshold uncertainty score0.281

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.297
Teacher spread0.280 · 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

Citations103
Published2007
Admission routes2
Has abstractyes

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