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Record W2409107290 · doi:10.1186/s13034-016-0103-x

Building research capacity in child welfare in Canada

2016· article· en· W2409107290 on OpenAlexafffundabout
Nico Trocmé, Catherine F. Roy, Thomas J. Esposito

Bibliographic record

VenueChild and Adolescent Psychiatry and Mental Health · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversité de MontréalMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAgency (philosophy)WelfareCapacity buildingPublic relationsService (business)Political scienceBusinessEconomic growthSociologyEconomicsMarketingSocial scienceLaw

Abstract

fetched live from OpenAlex

There is a surprising dearth of information about the services provided to the children and families being reported to Canadian child welfare authorities, little research on the efficacy of child welfare services in Canada, and limited evidence of new policies and programs designed to address these changes. This paper reports on a research capacity building initiative designed to address some of these issues. By fostering mutual co-operation and sharing of intellectual leadership, the Building Research Capacity initiative allows partners to innovate, build institutional capacity and mobilize research knowledge in accessible ways. The model rests on the assumption that by placing the university's research infrastructure at the service of community agencies, robust research partnerships are developed, access to agency-based research is significantly enhanced and community agencies make better use of research findings which all equate in greater research utilization and research capacity building.

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)0.116
metaresearch head score (Gemma)0.112
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.884
Threshold uncertainty score0.833

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1160.112
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.008
Science and technology studies0.0300.031
Scholarly communication0.0200.008
Open science0.0070.030
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0060.000

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 head0.282
GPT teacher head0.549
Teacher spread0.267 · 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.

Study designTheoretical or conceptual
DomainMethods
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

Citations27
Published2016
Admission routes3
Has abstractyes

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