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Record W7000960387

Imag(in)ing the Invisible

2025· article· en· W7000960387 on OpenAlexaff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBiomedicineBody politicEmerging technologiesDiseaseWork (physics)Human enhancement
DOInot available

Abstract

fetched live from OpenAlex

This commentary politicizes the relational-technical economy of biomedicine and the future it forecasts for feminized bodies with chronic illnesses. As digital medical imaging technologies develop, visualizations of disease are becoming more sophisticated. I begin by critically considering the implications this has for feminized bodies with chronic illnesses through the example of endometriosis, a common chronic pain disease that is not well understood within the biomedical paradigm. Enhanced imaging technologies promise to illuminate previously-unknowable aspects of disease pathophysiology, but what future is such technological progress enabling, and for whom? Through a critical intersectional lens, it becomes evident that the biomedical-technological future imag(in)es particular bodies, in particular places, and towards particular, but not unfamiliar ends. Enhancing abilities to visualize disease through digital technologies within a biomedical paradigm does not require us to look differently, which may be precisely what is needed. Thus, drawing theoretically on the work of bell hooks as well as critical feminist disability studies scholarship, I kindle the fire of a critical intersectional politic that transforms biomedical-technological ways of seeing the feminized body with chronic illness. Such a politic not only offers the possibility to imagine alternate futurities, but also contributes to their tangible realization.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.315
GPT teacher head0.642
Teacher spread0.327 · 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.

Study designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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