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Record W3135000168 · doi:10.4324/9781003145462

The Effectiveness of Educational Policy for Bias-Free Teacher Hiring

2021· book· en· W3135000168 on OpenAlexaboutno aff
Zuhra Abawi

Bibliographic record

Venuenot available
Typebook
Languageen
FieldComputer Science
TopicEducation and Learning Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPolitical sciencePedagogyMathematics educationEconomicsLabour economics

Abstract

fetched live from OpenAlex

This volume offers a critical examination of educational policy in Ontario, Canada, and critiques the success of such policies in ensuring diversity and equity of access in teacher hiring. Providing comprehensive coverage of historical marginalization in the Canadian education system, the book explains the rationale and objectives of policies enacted with the aim of ensuring "bias-free", or "colourblind" hiring. Drawing on qualitative data to illustrate how educators’ lived experiences often sit at odds with the inclusivity that such policies claim to achieve, the book presents the "Equity Hiring Toolkit" as a practical framework enabling educational administrators to recognize how unconscious biases and relative positions of power can implicate hiring decisions. This text will benefit researchers, doctoral students, and academics in the fields of teacher education, educational policy, and multicultural education more broadly. Those interested in the school leadership and management, as well as race and ethnic studies will also enjoy this volume.

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)0.031
metaresearch head score (Gemma)0.081
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.900
Threshold uncertainty score0.957

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.081
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0170.027
Scholarly communication0.0160.004
Open science0.0030.005
Research integrity0.0060.006
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 head0.039
GPT teacher head0.346
Teacher spread0.307 · 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 designObservational
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

Citations5
Published2021
Admission routes1
Has abstractyes

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