Bibliographic record
Abstract
The hospital’s ambiguous relationship to everyday social space has long been a central theme of hospital ethnography. Often, hospitals are presented either as isolated “islands” defined by biomedical regulation of space (and time) or as continuations and reflections of everyday social space that are very much a part of the “mainland.” This polarization of the debate overlooks hospitals’ paradoxical capacity to be simultaneously bounded and permeable, both sites of social control and spaces where alternative and transgressive social orders emerge and are contested. We suggest that Foucault’s concept of heterotopia usefully captures the complex relationships between order and disorder, stability and instability that define the hospital as a modernist institution of knowledge, governance, and improvement. We expand Foucault’s focus on the disciplinary, heterotopic qualities of the hospital to explore the heterotopia as a space of multiple orderings. These orderings are not only biomedical. Rather, hospitals are notable for the intensity and heterogeneity of the ongoing spatial ordering processes, both biomedical and other, that produce them. We outline an approach to heterotopias that traces the contingent configuration of hospital space through relationships between the physical environment, technologies, and persons, while simultaneously considering the kinds of spatial imaginings, hopes for the future, and emotional responses that are rendered possible by those configurations. We provide three thematic frameworks through which the heterotopic and contingent qualities of hospital spaces might be explored: boundary work, generating scale, and layered space.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.332 | 0.136 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".