Bibliographic record
Abstract
Lately, the premier ateliers of contemporary architecture -- such as Herzog & de Meuron, or the Office of Metropolitan Architecture -- are showing increasing interest in hospital design, once the realm of highly specialized architectural firms. This trend towards reevaluating hospital design and architecture is most opportune, as the COVID-19 pandemic urges us all to rethink the ways in which our healthcare institutions can be better designed. This commentary is a discussion on the emerging issues of contemporary hospital architecture, especially as reinforced by the pandemic. For instance, while hospital architecture today focuses on individualized care, providing each patient with hotel-like rooms, the pandemic has reminded us of the issue of capacity and inequality in these limited and costly spaces. To what extent should hospitals be centralized or decentralized? Specialized or despecialized? This commentary discusses how COVID-19 has provided insight into some of contemporary hospital architecture’s greatest problems; specifically, it argues that the hospital of the future must exist on a more decentralized platform, both physically and digitally, and be more flexible in function.
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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.043 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".