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Record W2102907777 · doi:10.25071/1918-6215.37455

Mapping Difference: Critical Connections between Crip and Diaspora Communities

2013· article· en· W2102907777 on OpenAlexfundno aff
Eliza Chandler

Bibliographic record

VenueCritical Disability Discourses · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDiasporaGeographySociologyGender studies

Abstract

fetched live from OpenAlex

This paper explores connections between crip and diaspora communities. I begin by discussing how the cultural production of racialized and disabled people are not analogous, but, rather, entangled. Following this, I reflect on a monologue by Leah Lakshmi Piepzna-Samarasinha that articulates the knot between the production of land and the production of disability. I then discuss how our understanding of geography is related to our understanding of the people who are placed or place themselves in particular geographic sites. I use Jasbir Puar’s concept of “debility” (2011) to unpack how the material and discursive production of people and land as disposable are also knotted. This paper ends by reflecting on how “unworking” (Walcott, 2003) our understandings of community and disability, the relationship between place and people, is one way of recognizing the “different stories of differences,” stories that challenges the mainstream disability right movement’s understanding of disability. Keywords: community; geography; diaspora; debility; environmental racism; biopolitics; neoliberalism

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)0.012
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 score0.021
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0210.064
Scholarly communication0.0100.016
Open science0.0020.018
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0040.000

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 head0.080
GPT teacher head0.328
Teacher spread0.248 · 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

Citations7
Published2013
Admission routes1
Has abstractyes

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