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Record W4237381894 · doi:10.1037/e611852012-001

Canadian Incidence Study of Reported Child Abuse and Neglect -2008: Major Findings

2010· dataset· en· W4237381894 on OpenAlexfundaboutno aff

Bibliographic record

VenuePsycEXTRA Dataset · 2010
Typedataset
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaDepartment of Social Services, Australian GovernmentPublic Health AgencyPublic Health Agency of Canada
KeywordsNeglectIncidence (geometry)Child abuseEnvironmental healthMedicinePsychologyPsychiatryInjury preventionPoison controlPhysics

Abstract

fetched live from OpenAlex

This report is the result of collaboration among federal, provincial and territorial government departments, university-based researchers, the First Nations representatives and, most important of all, child welfare service workers across the country who graciously participated in the data collection.The Canadian Incidence Study of Reported Child Abuse and Neglect (CIS) is one of the national surveillance programs of the Public Health Agency of Canada (PHAC), dedicated to the health of children in Canada in conjunction with other national surveillance programs on unintentional injury, perinatal health, and chronic and infectious diseases.Surveillance, which is a core function of public health, is a systematic process of data collection, expert analysis and interpretation, and communication of information for action on key health issues.

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)0.003
metaresearch head score (Gemma)0.013
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.034
Threshold uncertainty score0.248

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0090.025
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0050.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0100.006

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 head0.020
GPT teacher head0.311
Teacher spread0.291 · 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
GenreDataset

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

Citations133
Published2010
Admission routes2
Has abstractyes

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