Bibliographic record
Abstract
Every individual experiences good luck and bad luck. Three features characterize medical events associated with good luck or bad luck: There is no control over the event, the event occurs through chance or accident, and the event is of significant interest. These characteristics can be used to develop a working definition of medical luck. Medical good luck and medical bad luck are typically assigned to either the individual or to the event, but assigning these instead to the relationship between individual and event provides the opportunity for intervention. By assigning valences to each individual-event relationship and summating them, the total good luck or bad luck associated with the event can be determined. Intervening in the medical event by increasing the valence of the significance for each affected individual to the event will increase that event's total good luck. A total valence of zero before or after intervention does not, however, imply absent medical luck but simply a combination of medical good luck and medical bad luck because significance interest in the event persists. Therefore, there is no medical luck simpliciter, only medical good luck and medical bad luck. Medical events are especially helpful to understanding good luck and bad luck, because they are non-fictional, often generate significant interest, and are modifiable.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".