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Record W2910160612 · doi:10.51644/9781771123563

The Challenge of Children's Rights for Canada, 2nd edition

2018· book· en· W2910160612 on OpenAlexaboutno aff
Katherine Covell, R. Brian Howe, J.C. Blokhuis

Bibliographic record

Venuenot available
Typebook
Languageen
FieldMedicine
TopicEthics and Legal Issues in Pediatric Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsLibrary sciencePolitical sciencePsychologyComputer science

Abstract

fetched live from OpenAlex

More than a quarter of a century has passed since Canada promised to recognize and respect the rights of children under the United Nations Convention on the Rights of the Child. Ratification of the Convention cannot, however, guarantee that everyone will abandon proprietary notions about children, or that all children will be free to enjoy the substance of their rights in every social and institutional context in which they find themselves, including—and perhaps especially—within families. This disconnect remains one of the most important challenges to the recognition of children’s rights in Canada. The authors argue that social toxins are as harmful to children’s independent welfare and developmental interests as environmental toxins, and that both must be eradicated if Canada is to fulfill its commitments under the Convention. They also argue that if Canada wishes to ensure the substance of the rights outlined in the Convention are socially guaranteed, an attitudinal or cultural shift is required concerning the moral and legal status of children. This revised, expanded, and updated edition of the bestselling Challenge of Children’s Rights for Canada will be of interest to academics, policymakers, parents, teachers, social workers, and human service professionals—indeed to anyone who cares about and for children.

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)0.003
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 score0.133
Threshold uncertainty score0.514

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0090.006
Scholarly communication0.0090.004
Open science0.0010.002
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0190.006

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 head0.019
GPT teacher head0.305
Teacher spread0.286 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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