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Corporal punishment and reporting to child protection authorities: An empirical study of population attitudes in five European countries

2020· article· en· W3109790311 on OpenAlexaboutno aff
Kenneth Burns, Hege Stein Helland, Katrin Križ, Sagrario Segado Sánchez‐Cabezudo, Marit Skivenes, Judit Strömpl

Bibliographic record

VenueChildren and Youth Services Review · 2020
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsnot available
FundersEuropean Research CouncilUniversitetet i BergenNorges ForskningsrådEuropean Commission
KeywordsCorporal punishmentPunishment (psychology)Child protectionQuarter (Canadian coin)PopulationState (computer science)Intervention (counseling)PsychologyPolitical scienceSocial psychologyMedicineGeographyEnvironmental healthLawPsychiatry

Abstract

fetched live from OpenAlex

This study, which draws upon representative survey data of the populations of Austria (n = 1000), Estonia (n = 1069), Ireland (n = 1000), Norway (n = 1002) and Spain (n = 1000), compares population attitudes towards corporal punishment (CP) and whether citizens would report corporal punishment to the child protection authorities. We found significant cross-country differences in attitudes towards CP, but only small differences between countries in attitudes towards reporting it. The most interesting and puzzling finding was the mismatch between attitudes towards CP and attitudes towards reporting it: almost one third of individuals who reject CP would not report it, and a quarter of those accepting CP would report it. We discuss whether the observed mismatches are due to perceptions that the CP we described does not meet a threshold to require state intervention, and whether knowledge about bans of CP and/or moral obligations to report CP has impact. Furthermore, we discuss the role of populations’ confidence in the state and populations’ trust in the ability and competency of the child protection authorities to improve a child’s life.

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 score0.064
Threshold uncertainty score0.545

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.079
GPT teacher head0.363
Teacher spread0.284 · 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

Citations19
Published2020
Admission routes1
Has abstractyes

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