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Record W4404416391 · doi:10.1080/13540602.2024.2422859

How free are classroom teachers? Understanding teacher academic freedom in the United States and Canada

2024· article· en· W4404416391 on OpenAlexaffabout
David Waddington, Bruce Maxwell, Tessa MacLean, Kevin McDonough, Nasim Tavassoli

Bibliographic record

VenueTeachers and Teaching · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsDalhousie UniversityMcGill UniversityUniversité de MontréalConcordia University
Fundersnot available
KeywordsMathematics educationPedagogyAcademic freedomTeacher educationPsychologySociologyHigher educationPolitical science

Abstract

fetched live from OpenAlex

This paper presents the results of an international survey of teachers’ perspectives on academic freedom. We surveyed 173 social studies and science teachers from the United States and Canada about the importance of academic freedom, their comfort level broaching controversial topics in four broad areas (politics, economics, health and safety, and science), and the factors that influence their decision-making around discussing controversial topics. The study found that teachers value academic freedom despite being aware of how limited their freedom is in practice. Particularly controversial topics included safe injection sites, abortion, prisons, medically assisted dying, and same-sex marriage. Analysis via Mann-Whitney U tests was conducted to analyse whether teachers’ individual differences (e.g., experience, gender, or job status) influenced their pedagogical approach. Gender and years of experience had little influence, but teachers’ cautiousness was significantly influenced by whether they have tenure and the political climate of the area where they work.

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)0.005
metaresearch head score (Gemma)0.013
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 score0.103
Threshold uncertainty score0.750

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0230.009
Scholarly communication0.0080.002
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.109
GPT teacher head0.340
Teacher spread0.230 · 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

Citations7
Published2024
Admission routes2
Has abstractyes

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