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Record W4389099475 · doi:10.32920/24649341.v1

The Overrepresentation of Latin American Children in Canada’s Child Welfare System : Findings from the Canadian Incidence Study of Reported Child Abuse and Neglect-2019

2023· preprint· en· W4389099475 on OpenAlexaboutno aff
Henry Parada, Veronica Escobar Olivo, Barbara Fallon, Laura Best, Joanne Fillipelli, Emmaline Houston, Patricia Quan, Kristin Swardh

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsNeglectChild abuseChild protectionGeneral partnershipLatin AmericansWelfareMedicinePsychologyPolitical scienceDemographyPsychiatryEnvironmental healthPoison controlSuicide preventionNursingSociologyLaw

Abstract

fetched live from OpenAlex

This study examined Latin American children and families involved in Canadian child protection systems. It is a secondary data analysis, using data from the Canadian Study of Reported Child Abuse & Neglect, 2019, and a collaborative effort between the Rights for Children and Youth Partnership RCYP and the University of Toronto to: Provide the first report of national-level data on investigations involving Latin American children compared to white children, including: Investigating the type and severity of maltreatment Documenting caregiver, household and child characteristics of families investigated Monitoring short-term investigation outcomes such as placement; Ensuring the appropriate contextualization of findings; Disseminating research results to Latin American communities

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)0.005
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 score0.035
Threshold uncertainty score0.256

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.011
Science and technology studies0.0070.002
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.022
GPT teacher head0.274
Teacher spread0.252 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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