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Record W2746926195 · doi:10.1111/anti.12354

The Abstraction of Care: What Work Counts?

2017· article· en· W2746926195 on OpenAlexaff
Caitlin Henry

Bibliographic record

VenueAntipode · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Systems and Challenges
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHierarchyPoliticsWork (physics)AbstractionHealth careValue (mathematics)SociologyReproductionMetropolitan areaPublic relationsPolitical scienceMedicineEpistemologyComputer scienceLaw

Abstract

fetched live from OpenAlex

Abstract Nurses provide essential health care labor, but their work, a mix of caregiving and clinical expertise, is often undervalued and unacknowledged by health care administrators and the policies and practices that govern health care more broadly. Based on interviews with nurses working in the New York metropolitan area and through pairing feminist political economy with literature on abstraction and politics of the possible, I show that the ways in which nurses’ work is measured creates a value hierarchy of tasks. Examining various tools of measurement, I argue that methods for measuring work are rooted in an historical and continuous hierarchy of what counts as work and what has value. For nurses, these processes obscure the essential care work they perform. I argue that bringing an explicit politics of social reproduction to the politics of measuring and accounting for work makes visible necessary and often‐obscured tasks, spaces, and social relations.

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)0.020
metaresearch head score (Gemma)0.032
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 score0.024
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.004
Science and technology studies0.0150.117
Scholarly communication0.0240.022
Open science0.0020.011
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0040.001

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 head0.108
GPT teacher head0.484
Teacher spread0.376 · 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

Citations20
Published2017
Admission routes1
Has abstractyes

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