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Record W4361002932 · doi:10.1080/02722011.2023.2172885

On Teaching Human Rights History in a Settler Colonial Context

2023· article· en· W4361002932 on OpenAlexafffundabout
Laura Madokoro

Bibliographic record

VenueThe American Review of Canadian Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHuman rightsColonialismScholarshipContext (archaeology)SociologySubject (documents)IndigenousInsiderNarrativeEconomic JusticeLawEnvironmental ethicsPolitical scienceHistory

Abstract

fetched live from OpenAlex

Based on several years of experience in teaching human rights history to undergraduate students in Canada, this article reflects on the challenges involved in imparting knowledge on this subject in a settler colonial context. It builds on examples gleaned from working with undergraduate students, from scholarship on the history of settler colonialism, as well as from Indigenous worldviews and epistemologies, to consider the ways in which the teaching of human rights history needs to evolve alongside and in dialogue with contemporary discussions about rights and justice. The article contends that given contemporary discussions around rights, which reveal the fragility of the liberal human rights framework, this is urgent and necessary work. It concludes by offerings ways of approaching student experiences, insider/outsider dynamics, and contemporary debates when teaching human rights history. The overall purpose of the article is to resituate the teaching of human rights history in a critical, self-reflective manner. In this way, the damaging implications of certain progress-oriented historical narratives centering on the idea and evolution of human rights can also be considered in pedagogical practices on the subject.

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.098
Threshold uncertainty score0.570

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0250.033
Scholarly communication0.0080.003
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.048
GPT teacher head0.372
Teacher spread0.323 · 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
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 routes3
Has abstractyes

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