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Record W4394752096 · doi:10.1177/02637758241239158

Against abstraction: Reclaiming and reorienting to embodied collective knowledges of solidarity

2024· article· en· W4394752096 on OpenAlexafffund
Sarah Hunt, May Farrales

Bibliographic record

VenueEnvironment and Planning D Society and Space · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsSimon Fraser UniversityUniversity of Victoria
FundersCanada Research Chairs
KeywordsSolidaritySociologyAgency (philosophy)SovereigntyEmbodied cognitionEconomic JusticeGender studiesPolitical scienceEpistemologyLawSocial sciencePolitics

Abstract

fetched live from OpenAlex

This article asks what kinds of places are we making within academic institutions concerned with racial and gender justice, decolonization and reconciliation when the terms of justice are fostered through the violence of abstraction? As queer scholar-activists, we share firsthand accounts of our movement from sites of community activism to the halls of academe, revealing the techniques and consequences of abstracting racist and gendered violence and dispossession. We insist on a reorientation, meaning a return to and towards, our embodied collective knowledges as sources of authority on and against the violences of settler colonialism and racial capitalism. We first recount the roots of our activism when we met in spaces of solidarity in unceded Coast Salish territories. We then describe the ways we have experienced this collective knowledge being represented back to us as we moved into academic spaces, emphasizing the epistemic violence of abstracting knowledge from its messy, hard-wrought foundations. Finally, we share strategies and experiences of reorienting ourselves toward collective and embodied knowledges in which solidarity is once again at the centre. In dialogue, we reject the violence of abstraction while asserting knowledge sovereignty in which communities maintain agency within the terms in which their lives are represented.

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)0.003
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 score0.358
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.104
GPT teacher head0.425
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, 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

Citations7
Published2024
Admission routes2
Has abstractyes

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