Increasingly distant from life: problem setting in the organization of home care
Bibliographic record
Abstract
The analysis undertaken in this paper explores the significance of a central finding from a recent field study of home care case management practice: a notable feature of case management work is the preparation of an orderly, ordered space where care may be offered. However, out of their encounters with an almost endless variety of situations, out of people's diverse narratives of need, case managers seem able to pick out only limited range of recognized needs to which to respond and demonstrate a series of responses themselves equally limited. Though this observation suggests a kind of efficiency that is currently highly valued within healthcare systems, it also underlines the system's inability to engage difference and variability in a meaningful way. This inability or limitation in effectively engaging difference is conceptualized here as, in some sense, a problem, and the nature of this problem is explored through the rhetorical process of problem setting. The central question becomes how might we develop and deploy an orderly and coherent system of care without essentializing people's experiences, without treating these experiences reductively, without, in a Foucaultian frame of reference, allowing what can be understood as similarity or resemblance among clients and situations to be folded back into sameness? As we encounter complexity, variability and difference in practice, how should we treat it?
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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 | 0.044 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.030 | 0.102 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.007 | 0.012 |
| 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".