Enacting objects and subjects in a children’s rehabilitation clinic: Default and shifting ontological politics of muscular dystrophy care
Bibliographic record
Abstract
In health care clinics, problems are constructed through interactions, a choreography of human and non-human actors together enacting matters of concern. Studying the ways in which a body, person, family, or environment is objectified for clinical purposes opens discussion about advantages and disadvantages of different objectification practices, and exploration of creative ways to handle the diversity and tensions that exist. In this analysis, we explored objectifications in a Canadian neuromuscular clinic with young people with muscular dystrophy. This involved a close examination of clinical objectification practices across a series of 27 observed appointments. We identified the routinised clinical assessments, and argue these embed a default orientation to how to intervene in people's lives. In this setting, the routine focused on meeting demands of daily activities while protecting the at-risk-body, and working toward an abstract sense of an independent future for the person/body with muscular dystrophy. But the default could be disrupted; through our analysis of the routine and disruptions, we highlight how contesting visions for the present and future were consequential in ways that might be more than what is anticipated within rehabilitation practice.
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 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 | 0.027 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.029 | 0.102 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".