Bibliographic record
Abstract
Caring work, both professional and informal, supports the social networks that are key determinants of health. Caring theory reveals this work as gendered. Direct caring work, generally understood as women’s work, has been devalued and even rendered invisible. Men have been more involved in more visible executive caring. While direct caring is likely to be women’s work, technology has generally been associated with men and masculinity. This chapter shows how these two contrasting gendered processes of care and technology interweave at a particular community-based setting. The MacMorran Community Centre is located in a low-income neighbourhood of St John’s, Newfoundland, 1 and offers programmes to enhance the health and well-being of residents, including a programme of public access computers. Interviews with 25 key informants allowed us to explore the intersection of gendered processes involving technology on the one hand and caring on the other. We focus on ‘executive care’ (a more stereotypically masculine activity), ‘direct care’ (more stereotypically feminine) and receiving care, and on perspectives on the technology that may be intrinsic (more stereotypically masculine perspective) or instrumental (more stereotypically female). Our analysis reveals more involvement of men in caring (both executive caring and more visible, direct care), a development that appeared to enhance the community’s abilities to support the health of its members. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".