The moving of St Vincent's: a tale in two cities
Bibliographic record
Abstract
In Australia, demographic changes have seen the population of large cities move away from the inner city. This, combined with changes in healthcare delivery and the ageing of many tertiary teaching hospitals, has led governments to attempt to close, relocate or redefine the role of some institutions. Tracing the media coverage of two such events--the attempts to move St Vincent's hospitals in Sydney and Melbourne--provides some interesting insights into the challenges of resource allocation facing policymakers within the healthcare sector. Both hospitals were long-established, much-loved fixtures on inner-city sites with powerful connections to government and business. In Sydney, where the attempt was part of a larger plan to reallocate resources to the western suburbs, the announcement was met with 10 days of intense media coverage and scrutiny by lobby groups and the general public. By contrast, in Melbourne, no such announcement was made and the low-key reporting of support and opposition to the move occurred over two months. Both attempts failed. No matter how the debate is handled, radical changes involving long-established hospitals, powerful provider groups and loyal communities are very difficult to accomplish.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.040 | 0.013 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".