‘What she says she needs doesn’t make a lot of sense’: seeing and knowing in a field study of home‐care case management
Bibliographic record
Abstract
Foucault's preoccupation with the visual, specifically his positing of a sort of 'positive unconscious of vision', offers an entry point for examining data generated through a field study of home-care case management practice. In Foucault's work, our attention is directed not so much to what is seen but to what can be seen and to the effects of practices of knowledge and power in constituting these particular realities. Knowledge emerges as a matter of what it is possible for knowers, for nurses, to see and to say, as well as the conditions that constitute these specific possibilities for seeing and saying in a given context. Given the significance of practices of seeing in case management - seeing clients, seeing situations - examining how possibilities for understanding are constituted through ways of seeing helps us to 'see' the limits of currently possible practice. In the case examined in this paper, these limits constitute a gap between what a client may actually need and what it is possible, in the context of current practice, to provide. To change practice it seems important, if only as a first step, to recognize the constraints of thought in what we see.
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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 | 0.049 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.034 | 0.067 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".