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Record W4387696170 · doi:10.1111/1467-9566.13722

Diminished faculties: A political phenomenology of impairment. By J.Sterne, Durham, NC: Duke University Press. 2022. pp. 304. $102.95 (cloth); $27.95 (pbk). ISBN: 978‐1478015086

2023· article· en· W4387696170 on OpenAlexaboutno aff
Magda Szarota

Bibliographic record

VenueSociology of Health & Illness · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsCitationPoliticsPhenomenology (philosophy)Library scienceMedia studiesSociologyPhilosophyPolitical scienceLawComputer scienceEpistemology

Abstract

fetched live from OpenAlex

‘[T]his is really a story about how to exist in a changed body and how to negotiate that change. It is not meant to be offered as a lament or a form of mourning. I experienced a change in orientation, and phenomenology is all about orientation’ (p. 12). ‘I’ in this quote stands for Jonathan Sterne, a culture and technology professor at McGill University in Canada, and the author of ‘Diminished Faculties: A Political Phenomenology of Impairment’. This book aims to dissect impairment, disability, illness, fatigue, power and (assistive) technology in relation to each other. It does so in the context of capitalism, mainly that of Canada and the US. Sterne’s book is a fantastically meta work, in which the academic rigour and theoretical sophistication not only make room for but are also enhanced by formal experimentation and…. humour.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.043
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.015
Scholarly communication0.0050.008
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.042
GPT teacher head0.310
Teacher spread0.268 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2023
Admission routes1
Has abstractyes

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