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Record W2796934453 · doi:10.60082/0829-3929.1294

Issue 1: Reimagining Child Welfare Systems in Canada

2018· article· en· W2796934453 on OpenAlexaffvenueabout
Janet Mosher, Jeffery Hewitt

Bibliographic record

VenueJournal of Law and Social Policy · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicResearch in Social Sciences
Canadian institutionsYork University
Fundersnot available
KeywordsWelfare systemWelfareSociologyPolitical scienceGender studiesLaw

Abstract

fetched live from OpenAlex

The removal of children from their families and communities has long-lasting and often devastating consequences. The breaking of the bonds that connect children to family and community erodes the transmission of culture, of language, of belonging, and of identity. There is no doubt that there are circumstances in which the well-being of a child requires that changes be made in who is to be entrusted with the responsibility for care. However, there is equally no doubt that child welfare systems across the country are plagued by serious shortcomings: bonds are broken unnecessarily; structural failings undermine parents’ well-being and their capacity to provide adequately for their children; children, once removed, have limited or no access to their cultural roots; and racism is embedded in structures, practices, and individual encounters.

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)0.007
metaresearch head score (Gemma)0.018
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.720
Threshold uncertainty score0.835

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0340.007
Scholarly communication0.0140.003
Open science0.0070.007
Research integrity0.0100.008
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 head0.381
Teacher spread0.348 · 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

Citations10
Published2018
Admission routes3
Has abstractyes

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