MétaCan
Menu
Back to cohort
Record W4383896490 · doi:10.36510/learnland.v16i1.1109

Teacher’s Choice: Agents of Harm or Help? Innovation as a Lever for Social Justice and Intersectionality

2023· article· en· W4383896490 on OpenAlexvenueaboutno aff
Sabrina Jafralie

Bibliographic record

VenueLEARNing Landscapes · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsHarmIntersectionalityDiversity (politics)CurriculumSocial justiceLeverEconomic JusticeSociologyPedagogyPublic relationsEngineering ethicsPolitical sciencePsychologyCriminologySocial psychologyEngineeringLawGender studies

Abstract

fetched live from OpenAlex

As educators, the need to adapt, change, and help students is always at the forefront. Today, there is a growing demand for teachers to innovate the curriculum to ensure accessibility and representation of student diversity as well as address inequities in education. This is an educator’s professional reflections on the relationship between innovation in education and the use of social justice in Quebec’s pedagogy, how to reduce injustices in the classrooms, and why it is necessary.

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)0.022
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.098
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0140.098
Scholarly communication0.0190.016
Open science0.0020.011
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0080.001

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 head0.094
GPT teacher head0.402
Teacher spread0.308 · 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 designNot applicable
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
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueLEARNing LandscapesSame topicSocial Sciences and GovernanceFrench-language works237,207