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Record W2176054694 · doi:10.22329/jtl.v10i1.3696

Brave New Teachers: Doing Social Justice Work in Neo-liberal Times

2015· article· en· W2176054694 on OpenAlexaffvenueabout
Barbara Pollard

Bibliographic record

VenueJournal of Teaching and Learning · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsNeoliberalism (international relations)CurriculumContext (archaeology)Diversity (politics)AccountabilityDemocracySocial justiceSociologyPedagogyWork (physics)Economic JusticePolitical sciencePoliticsSocial scienceLawEngineering

Abstract

fetched live from OpenAlex

The authors of Brave New Teachers adopt a critical-democratic lens to analyze how neoliberal educational agendas have impacted the ability of progressive teachers to cultivate equitable and inclusive classrooms. The book is based on a longitudinal study that documents the self-reported teaching experiences of a group of teachers who graduated from the Urban Diversity Program at York University. Brave New Teachers explores how the current context of Canadian schooling has been shaped by neoliberalism. Within this paradigm, the authors thematically emphasize how the standardized testing and accountability movement, as well as the top-down imposition of curriculum standards, circumscribe the efforts of courageous teachers who work diligently on creating anti-oppressive curriculums and school environments. The tension that exists when these teachers feel pressured to conform to higher top-down authorities is documented throughout the` book.

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)0.002
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.147
Threshold uncertainty score0.292

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0200.025
Scholarly communication0.0090.007
Open science0.0010.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.100
GPT teacher head0.394
Teacher spread0.293 · 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

Citations29
Published2015
Admission routes3
Has abstractyes

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