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Record W2936275617

Searching for Feminist Discourse in Social Justice Mathematics

2018· article· en· W2936275617 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2018
Typearticle
Languageen
FieldPsychology
TopicEgo Development and Educational Practices
Canadian institutionsYork University
Fundersnot available
KeywordsTransformative learningCurriculumPatriarchyGender studiesSociologyFeminismCritical consciousnessFeminist pedagogyEconomic JusticeSocial justicePedagogySocial sciencePolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this paper is to understand how the intended transformative potential of social justice mathematics (SJM) curricula about the social problems of sexism, patriarchy and associated gender injustices might be strengthened when the role of discourses is considered.  Through feminist critical discourse analysis we examined the extent to which a middle school curriculum designed to highlight missing and murdered Indigenous women is a consciousness-raising text with respect to sexism and associated injustices. We found the lesson to be a strong example of a potentially transformative learning experience about gender injustices that partly stem from sexist discourses.  However, we share the teacher’s view that the activity , like his other gender justice-oriented lessons, cannot capture all of the complexity of the issues connected to its central topic.  Mathematics is an important tool for understanding social realities.  Viewing the SJM curricula through a feminist discursive lens opens the door to further research that contrasts the approach in the lesson to what we see as a pervasive patriarchal discourse in schooling and society.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.107
GPT teacher head0.429
Teacher spread0.321 · 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