Bibliographic record
Abstract
Editor—In his editorial Jones described the decline of altruism in medicine.1 As Claire Luce Booth—a congresswoman, ambassador, playwright, socialite, and wife of American magazine magnate Henry R Luce—noted, “No good deed goes unpunished.”2 Business authors have described that individuals in the employ of service industries (such as medicine) have two levels of service to offer. The most basic level of service is the performance of specified duties only, just enough to avoid being fired and nothing more. All the rest is “volunteer” work: going the extra mile for patients, doing more than you have to do, including doing some of it for free, are all efforts in excess of that which can be compelled by a job description. Love of one's work and patients begets such volunteering. Such is the way of altruism. When volunteerism declines in any service industry, the blame falls on the work environment. As doctors learn that there is little or no emotional or financial reward for voluntary addenda to patient care and that such behaviours may actually be punished, the natural tendency is to work to the rule. Only that which is required gets performed. Punishment of physicians is indeed effective, but the results are not those intended by administrators, lawyers, and regulators.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.044 | 0.041 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".