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Record W4402734046 · doi:10.18357/otessaj.2024.4.1.62

Towards a critical co-construction of equity communities of practice in education

2024· article· en· W4402734046 on OpenAlexaffvenueabout
Lorayne Robertson, Jessica Trinier, Roland van Oostveen

Bibliographic record

VenueThe Open/Technology in Education Society and Scholarship Association Journal · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsEquity (law)Critical practiceBusinessSociologyPolitical scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

Students in Canada have unequal access to safe environments and learning in schools, which impacts their participation in education and their achievement of educational goals. Equity, diversity, and inclusion (EDI) courses for future educators are one way to help them see that the benefits of schooling are not equally available to all students. The authors describe how post-secondary students, who were members of equity-seeking communities and their allies, worked together with instructor guidance to co-create EDI courses. The students were already familiar with the fully online learning community model (FOLC) where student voice and agency feature prominently. As the students co-designed new EDI courses, the critical co-construction of equity model was developed. The model is anchored in human rights and relies on a shared spirit of equity humility. The model recognizes the need for student safety as well as the necessity of potentially uncomfortable conversations. While, in the past, equity teaching tended to focus on distinct aspects of oppression in society as individual topics, the co-construction of equity model relies, instead, on building bridges of equity concepts that cross oppressions. These cross-equity understandings can help future educators see the importance of dismantling oppression and rebuilding safer and more inclusive learning spaces in education.

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)0.014
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.002
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 head0.073
GPT teacher head0.499
Teacher spread0.425 · 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 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 routes3
Has abstractyes

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