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Record W4391921132 · doi:10.33524/cjar.v24i1.664

Addressing the Subjugation of Knowledge in Educational Settings through Structuration of Teacher Research

2024· article· en· W4391921132 on OpenAlexaffvenue
Will Edwards, Amir Kalan

Bibliographic record

VenueThe Canadian Journal of Action Research · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsMcGill UniversityGeorge Brown College
Fundersnot available
KeywordsAgency (philosophy)SociologyWork (physics)Action (physics)IndigenousAction researchStructure and agencyEducational researchFocus (optics)PedagogyTraditional knowledgeEngineering ethicsEpistemologySocial scienceEngineering

Abstract

fetched live from OpenAlex

Building on critical sociological models and action research traditions, our work theorizes a structurated model of action research to address the subjugation of knowledge within educational settings. We focus on the interplay between structure and agency and how these dimensions can co-evolve in teacher research. In this article, we examine how teachers and researchers engaged in collaborative inquiry communities inhabit a complicated role within educational structures. The authors outline and detail rich cases that illustrate the dense particulars of knowledge subjugation within educational structures—these range from the denigration of immigrant students’ credentials to the suppression of indigenous languages. The testimonies of practitioners and students are presented to underscore the inchoate and contradictory conditions that inform educational systems and the meaningful alternative practices that might contravene inequitable structures. The possibilities for recognizing the corrosive mechanisms of knowledge subjugation potentiate resistant parallel structures that invite meaningful inquiry-based methods.

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)0.106
metaresearch head score (Gemma)0.094
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.979
Threshold uncertainty score0.563

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1060.094
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.003
Science and technology studies0.0210.183
Scholarly communication0.0290.025
Open science0.0050.032
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0030.000

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 head0.643
GPT teacher head0.622
Teacher spread0.022 · 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.

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

Citations0
Published2024
Admission routes2
Has abstractyes

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