Bibliographic record
Abstract
Sociological research in a large organization necessarily makes demands upon the lives of many people.It either asks for and receives their help or, at the very least, imposes on their time, almost demanding tolerance.It would be difficult to list all who contributed to and/or tolerated this study, even if anonymity did not forbid it.We can at least express our gratitude to some individuals and categories of people and again beg their tolerance.Veterans at Westfield Hospital, especially patients in our project and control wards, put up with a lot from us.Their routines were disturbed.They were often asked questions that must have seemed inane.We can only profoundly thank them for their enthusiastic participation in the project.Without it, nothing could have been achieved.The head nurse and other nurses of the project ward, along with patients, bore the major burden of our attempts at change.Despite differences of opinion, they shouldered the burden conscientiously, with courage and hard work.We learned from them and are grateful for their efforts.A long list of people (nurses, physicians, administrators, orderlies, psychologists, physiotherapists, occupational therapists, and volunteers) also contributed understanding, skill, enthusiasm, and time to the project.I can only mention the following research staff: Mr. Mac
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.215 | 0.118 |
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".