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Record W2950337693 · doi:10.1177/0886260519852631

How Do Knowledge and Attitudes About Children’s Rights Influence Spanking Attitudes?

2019· article· en· W2950337693 on OpenAlexaffabout
Elena Gallitto, Gabrielle Josée Veilleux, Elisa Romano

Bibliographic record

VenueJournal of Interpersonal Violence · 2019
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSpankingCorporal punishmentPsychologyDignitySocial psychologyMultilevel modelPunishment (psychology)Human factors and ergonomicsDevelopmental psychologyPoison controlPolitical scienceMedicineLawEnvironmental health

Abstract

fetched live from OpenAlex

Children's rights are about treating children with equality, respect, and dignity. Attitudes concerning children's rights have been linked to support for nurturance and self-determination. However, there is little research on how dimensions of children's rights are associated with other parenting constructs, such as attitudes toward physical punishment. This study examined the relationship between knowledge of and attitudes toward children's rights and attitudes toward spanking in a Canadian sample of 329 undergraduate students who completed an online study. Hierarchical multiple regression analyses indicated a significant negative association in that more favorable attitudes toward children's rights predicted less favorable attitudes toward spanking. There also was a significant moderating effect of child rights knowledge on this relationship, such that greater knowledge enhanced the effects of attitudes toward children's rights on spanking attitudes. These results raise awareness of the combined role of both knowledge of and attitudes toward children's rights in influencing spanking attitudes. The results also suggest that one pathway decreasing favorable attitudes toward spanking may be to increase the general public's knowledge of children's rights.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.022
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.289
Teacher spread0.279 · 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 teacher head, 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

Citations9
Published2019
Admission routes2
Has abstractyes

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