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Record W2578147543 · doi:10.1080/13562517.2016.1273214

Doing feminist difference differently: intersectional pedagogical practices in the context of the neoliberal diversity regime

2017· article· en· W2578147543 on OpenAlexaffabout
Sandra Smele, Rehanna Siew-Sarju, Elena Chou, Patricia Louise Breton, Nicole S. Bernhardt

Bibliographic record

VenueTeaching in Higher Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsWomen's and Gender Studies et Recherches FéministesYork University
Fundersnot available
KeywordsIntersectionalityNeoliberalism (international relations)SociologyContext (archaeology)FeminismRacismDiversity (politics)Power structureUndoingGender studiesHigher educationCritical pedagogyBlack feminismPoliticsPedagogySocial sciencePolitical sciencePsychologyLaw

Abstract

fetched live from OpenAlex

At present there is a small, albeit growing, body of literature on pedagogical strategies and reflections which addresses the ways educators attempt to challenge the effects of neoliberalism on higher education. In this article, we reflect upon our pedagogical practices in higher education in this moment of neoliberal transformation wherein, as Sirma Bilge notes, intersectionality is being ‘undone’ in academic feminism. As graduate students teaching in Toronto, Canada, we describe how our commitment to social justice pedagogy works against this ‘undoing’ of intersectionality by embracing vulnerability, discomfort and the possibility of conflict in classrooms that do not simply accommodate, celebrate or include difference. Given that neoliberal renderings of diversity obscure and reinforce unequal relations of power, we demonstrate how we attend to these power relations, particularly racism which is salient to our teaching context, by employing intersectionality as a pedagogical practice and a political intervention to advance social justice.

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)0.029
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.029
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0290.094
Scholarly communication0.0160.013
Open science0.0030.027
Research integrity0.0040.011
Insufficient payload (model declined to judge)0.0050.001

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 head0.315
GPT teacher head0.420
Teacher spread0.104 · 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 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

Citations37
Published2017
Admission routes2
Has abstractyes

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