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Record W4403501172 · doi:10.28968/cftt.v10i1.42961

Troubling Transplant Temporality through Crip Technoscience and a Sensory Aesthetics of Time, Machine, and Health

2024· article· en· W4403501172 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCatalyst Feminism Theory Technoscience · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsCarleton UniversityUniversity of Toronto
Fundersnot available
KeywordsTemporalityTechnoscienceAestheticsArtPhilosophyEpistemology

Abstract

fetched live from OpenAlex

Taking up technologies that act as a “bridge” to transplant, we explore what the lifeworlds of a transplant patient and their loved ones interacting with these technologies tells us about the temporality of transplantation and the imperative of cure.By engaging with clinical literature and practices, transdisciplinary crip technoscience theorizing, and multimodal, multisensory artistic form, we bring together sick and disabled people’s expertise and diverse ways of knowing that enable a rich and nuanced exploration of transplant time that complicates biomedical expertise while at the same time recognizes its embrace as one with opportunity and potential for care and collaboration. In troubling transplant temporality through crip technoscience and a sensory aesthetics of time, machine, and health, we demonstrate how perspectives often excluded or minimized in acute care offer innovative ways in which to think through the material limitations of taken for granted understandings within transplant settings.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptScience and technology studies
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.006
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.038
GPT teacher head0.347
Teacher spread0.309 · 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