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Child Abuse and Neglect in Canada

2019· book· en· W4238942499 on OpenAlexaffabout
Lea Tufford

Bibliographic record

Venuenot available
Typebook
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsLaurentian University
Fundersnot available
KeywordsNeglectChild abusePsychologyCriminologyChild neglectPolitical sciencePsychiatryDevelopmental psychologyMedicineMedical emergencySuicide preventionPoison control

Abstract

fetched live from OpenAlex

Abstract Child Abuse and Neglect in Canada: A Guide for Mandatory Reporters offers a concise guide to mandatory reporting in provincial and territorial jurisdictions with specific attention to the context and unique realities of northern Canada. As an introduction to mandatory reporting, the book opens with an exploration of the historical rise of the child welfare system, mandatory reporters’ ethical duties around reporting, types of abuse and neglect, risk and protective factors, and the ascendancy of child abuse in an online environment. The latter half of the book first explores decision-making factors (legal, clinician, situational, professional, and relationship) to assist human service professionals with their decision-making. This section then explores the reporting process and offers relationship repair strategies (reporting, information, affect regulation, advocacy, resource, and cultural). The book culminates in a comprehensive, empirically based conceptual framework to assist human service professionals with decision-making and maintaining the relationship. Predicated on the author’s dissertation research Child Abuse and Neglect in Canada: A Guide for Mandatory Reporters offers students a comprehensive framework for fulfilling their professional, fiduciary obligations and provides educators with accessible teaching tools to further their students’ understanding of this area.

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)0.000
metaresearch head score (Gemma)0.001
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: Other
Teacher disagreement score0.046
Threshold uncertainty score0.331

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0060.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.002

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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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

Citations2
Published2019
Admission routes2
Has abstractyes

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