The Semiotics of Western Hospitals: From a Stone Boat in Rome to Reconstructing the Self in Montreal
Bibliographic record
Abstract
In this article I contrast the semiotic role of the hospital from its creation in Rome in the 2nd century BC with several contemporary Montreal hospitals. The hospital was founded as a site to isolate the sick to limit the symbolic pollution of the allegedly perfect social body of the Roman state. Today, however, the hospital has become a semiotic engine that allows patients to construct a new temporal matrix and affirm their individuality to counter contemporary hospital practices that standardise patients according to their illness while ignoring patients’ biographies. I propose that patient narratives in the modern context use the hospital as raw material to construct a temporal framework that substitutes the rhythms of everyday life that illness and the institutional culture of the hospital have interrupted. These narratives adhere to the same basic structure: the entrance scenario is always admission to the hospital; the plot structure is built with the non-medical details of the daily hospital routine. Surrounded by a neoliberal ethos that insists on the autonomy of the self and silenced by the mechanisation of illness, contemporary patients transform hospitals into semiotic engines where patients use their immediate environment to re-engineer new voices of the self. In other words, hospitals are sites where people combat depersonalisation with new biographies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.044 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".