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Record W4411994075 · doi:10.15353/cjds.v12i1.969

Building a Community for Queer Disability Studies: Lessons from the Snail

2023· article· en· W4411994075 on OpenAlexvenueno aff
Harvey Humphrey, Edmund Coleman-Fountain, Charlotte Jones

Bibliographic record

VenueCanadian Journal of Disability Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsnot available
Fundersnot available
KeywordsQueerSnailDisability studiesSociologyGender studiesEcologyBiology

Abstract

fetched live from OpenAlex

This article describes the Queer Disability Studies Network, a space set up for Queer Disability Studies academics and activists to find solidarity, particularly those experiencing marginalisation due to queerphobia, transphobia, intersexphobia and ableism in Disability, Queer, Trans and Intersex Studies; and for ideas in these disciplines to inform one another. The network was established to oppose the institutionalisation of ideas that would delegitimise trans lives and identities within academia and provides a space of solidarity and resistance within the neoliberal- ableist university. The article provides an explanation of the origins of the network. From this it uses the network’s snail motif to organise learnings from Trans, Queer, Intersex and Disability Studies into a set of ‘lessons’ for groups seeking to develop solidarities within academic and activist communities. These lessons raise critical questions related to concepts of 1) home, 2) temporalities and mobilities, and 3) embodiments and vulnerabilities. We conclude by discussing the implications of these lessons for practising solidarities and coalitional politics in contested times.

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)0.009
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.065
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0330.047
Scholarly communication0.0140.016
Open science0.0020.020
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0150.001

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 head0.271
GPT teacher head0.472
Teacher spread0.201 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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