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Record W4399384919 · doi:10.1177/14778785241257176

Impartiality, human rights advocacy, and teaching about politically sensitive issues: Squaring the circle

2024· article· en· W4399384919 on OpenAlexaff
Bruce Maxwell

Bibliographic record

VenueTheory and Research in Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsImpartialityHuman rightsPolitical scienceHuman rights educationLaw and economicsSociologyPublic administrationEnvironmental ethicsLawPhilosophy

Abstract

fetched live from OpenAlex

This article first describes and then proposes a practical solution to the professional dilemma between the duty of impartiality and the duty of human rights advocacy that many teachers experience when teaching and talking about politically sensitive issues with students. The article begins by presenting an analysis of the source and signification of the tension between impartiality and human rights advocacy based on evidence from research on teachers’ perspectives, the conceptual literature on teaching and learning about controversial issues, and the legal and ethical framework of education. Then, drawing on scholarship on respect for students’ right to freedom of religion, the article advances and defends set of basic pedagogical guidelines for teaching and talking about politically sensitive issues that permit teachers to maintain a professional stance of impartiality without abrogating their responsibility to act as human rights advocates. Key to squaring the circle between impartiality and human rights advocacy, the article argues, is for teachers to strive to remain descriptive in their treatment of politically sensitive issues and insist on high standards of reasoning and evidence while at the same time respecting students’ right to an opinion, no matter how mistaken that opinion may seem.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.116
GPT teacher head0.532
Teacher spread0.416 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations9
Published2024
Admission routes1
Has abstractyes

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