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Record W2259004295 · doi:10.7870/cjcmh-2014-013

Trends in Corporal Punishment and Attitudes in Favour of This Practice: Toward a Change in Societal Norms

2014· article· en· W2259004295 on OpenAlexaffvenueabout
Marie‐Ève Clément, Claire Chamberland

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2014
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversité de MontréalUniversité du Québec en Outaouais
Fundersnot available
KeywordsCorporal punishmentPsychologyPunishment (psychology)Telephone surveyPopulationSpankingCriminologySocial psychologyHuman factors and ergonomicsPoison controlDemographySociologyMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Over 10 years ago, Quebec's statistical institute, the Institut de la Statistique du Québec, conducted its first population survey on family violence in the lives of children. Taken again 5 years later (2004), the survey showed a decrease in the use of corporal punishment of children. This paper presents the results of the third survey, which was conducted by telephone in 2012 with a representative sample of 4,029 maternal figures. More specifically, this paper presents the evolution of prevalence rates for corporal punishment and attitudes in favour of this practice as observed between the 3 surveys conducted in 1999, 2004, and 2012. The paper also presents the associations found between mothers’ attitudes and their reports of corporal punishment. The results show that attitudes and practices are closely associated and that corporal punishment has been in a constant and significant decrease since 1999, regardless of the children's age. Supported by a decrease in maternal attitudes in favour of this practice, the results are discussed in terms of changes in Quebec's social norms.

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)0.005
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 score0.257
Threshold uncertainty score0.517

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.111
GPT teacher head0.402
Teacher spread0.292 · 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

Citations51
Published2014
Admission routes3
Has abstractyes

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