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Record W2114457386 · doi:10.26522/tl.v7i3.422

Teaching & Learning about Humanitarian Law: Perspectives from Canadian Teachers

2013· article· en· W2114457386 on OpenAlexaffvenueabout
Catherine Baillie Abidi, Mary Jane Harkins

Bibliographic record

VenueTeaching and Learning · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicPeace and Human Rights Education
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsInternational humanitarian lawDignityCurriculumPolitical sciencePedagogyLawSociologyHuman rightsPublic relations

Abstract

fetched live from OpenAlex

Respect for human dignity and appreciation for diverse understandings are two quintessential elements of peaceful practices. Education focused on respect and the consequences of war, is essential for today’s youth, given the global impact of war. International humanitarian law was established to promote respect during war, to protect civilians and those no longer fighting. Humanitarian law education can create space for critical consciousness, self-reflection, and active citizenship. This study explores Canadian teachers’ experiences teaching and learning about social justice, war, and conflict, through the educational resource, Exploring Humanitarian Law (EHL). The main findings include (1) the importance of helping students to discover meaningful, real world connections, (2) teachers’ perceptions about the relevancy of international humanitarian law education, (3) tools for engaging all students, and (4) the constraints and challenges of implementing humanitarian law curriculum. Recommendations for practice and future areas for research are suggested.

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)0.006
metaresearch head score (Gemma)0.012
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.763

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0570.016
Scholarly communication0.0100.004
Open science0.0020.004
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0050.001

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 head0.020
GPT teacher head0.294
Teacher spread0.274 · 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
Published2013
Admission routes3
Has abstractyes

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