Forming Bodies and Reforming Healthcare: The Co-Construction of Information Technologies and Bodies through the Imperative for Self Care
Bibliographic record
Abstract
Care work and technological work are markedly striated by sex; the sites where they overlap are few. What happens when the labor of care meets up with information technologies? It makes good methodological sense to look at largely feminized environments that are also increasingly technological. Gender, Health, and Information Technology in Context, edited and with contributions by Ellen Balka, Eileen Green, and Flis Henwood, is a welcome contribution to the body of evidence about the socio-technical co-construction of technology, health, and gender. The volume houses nine studies, bookended by an astute introduction and conclusion by the editors. Each study brings empirical research to bear on technology and gender in health contexts. The studies originate from the United Kingdom, Canada, and Australia, and from multiple sites of practice, including clinics, hospitals, community centers, libraries, and health outreach. Each of the nine chapters is based on theoretically grounded qualitative research. The represented theoretical approaches make connections between computer-supported cooperative work (CSCW), science and technology studies (STS), feminist epistemology, feminist science studies, labor studies, library and information science, and care work. Thus this volume is of interest to multiple audiences. It is equally appropriate to nursing, health sciences, information studies, and labor studies. It is also a helpful resource for those looking at the labor of care, both in nursing and in other care-based or feminized professions, and particularly those facing transformation of work routines through new information technologies. Informants include patients, nurses, health intermediaries, social workers, and other hospital workers. This collection will be valuable to anyone looking for empirical examples and studies of the intersection of women’s labor and technology, labor of care and technology, or gender and technology more broadly construed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.080 |
| Scholarly communication | 0.027 | 0.022 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".