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Record W7057962475

Learning and knowing their rights: the inclusion of mandatory international human rights education in Ontario secondary school curriculum

2023· dissertation· en· W7057962475 on OpenAlexaffabout

Bibliographic record

VenueMspace (University of Manitoba) · 2023
Typedissertation
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
Fundersnot available
KeywordsCurriculumHuman rightsHuman rights educationInclusion (mineral)AutonomyCompulsory educationNational curriculumPresentation (obstetrics)
DOInot available

Abstract

fetched live from OpenAlex

This paper explores the need to implement a compulsory international human rights curriculum in the Ontario education system as a means of empowering children to actualize their rights and stand up against human rights violations. The importance of children’s participation in civil society and respect for their autonomy is discussed, followed by a presentation of the relevant international human rights treaties that support this proposal. The current high school curriculums and courses in Ontario are analyzed to demonstrate the gaps in relevant human rights content, as well as where such content would be appropriate, as there is currently no compulsory course in place. To establish that such a curriculum is not just important but also effective, the success and review of the Australian national curriculum are discussed. Following the Australian case, recommendations for the Ontario Ministry of Education are presented to demonstrate that ameliorating the human rights curriculum in the province is attainable and would be successful in educating the next generation of change-makers.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.412

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.003
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.007
GPT teacher head0.223
Teacher spread0.216 · 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 designQualitative
Domainnot available
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

Citations0
Published2023
Admission routes2
Has abstractyes

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