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Record W4410300261 · doi:10.32920/29041835.v1

Strengthening Institutional Responses: Criminal Justice, Child Welfare, and Immigration Systems

2025· preprint· en· W4410300261 on OpenAlexfundaboutno aff
Marsha Rampersaud, Henry Parada, Kristin Swardh, Patricia Quan, Veronica Escobar Olivo

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of WaterlooYork University
KeywordsImmigrationCriminal justiceWelfareCriminologyEconomic JusticeWelfare systemPolitical scienceSociologyLaw

Abstract

fetched live from OpenAlex

In August 2022, the three-day Strengthening Institutional Responses conference took place in Toronto, Ontario. This conference was hosted by the Rights for Children and Youth Partnership (RCYP) in collaboration with the Child Welfare Immigration Centre of Excellence (CWICE), Legal Aid Ontario (LAO), StepStones for Youth, Toronto Metropolitan University, and the University of Waterloo, and received generous funding from the Social Sciences and Humanities Research Council. A primary objective of the conference was to shed light on young people’s experiences with child welfare, criminal justice, and immigration systems, emphasizing the Ontario context.

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)0.006
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.768
Threshold uncertainty score0.672

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0250.026
Scholarly communication0.0110.004
Open science0.0020.015
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.000

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 head0.033
GPT teacher head0.342
Teacher spread0.309 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2025
Admission routes2
Has abstractyes

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