Bibliographic record
Abstract
The following is adapted from a talk given by the author, a former member of the National Council of the American Federation for Medical Research, at the Heart Failure Society of America Annual Scientific Meeting, Toronto, Ontario, Canada, on September 23, 2008. The issue I would like to address has to do with what many of us perceive to be a slow progress in research leading to the development of new therapeutic agents for heart failure. I would argue that our academic paradigm, with its focus on assessing the individual accomplishments of faculty members rather than on facilitating progress by the research community as a whole, is an impediment to progress. I would like to begin my argument using, as an allegorical example, a story about a basketball team. The owner of a basketball team wanted to devise an incentive plan to improve his team's performance. He reasoned that team performance was the sum of the performances of the individual players, and that the key, therefore, was to maximize the productivity of each individual. He reasoned further that, because basketball games are won by scoring more points than one's opponent, the appropriate incentive plan would be one in which each player would be paid in direct proportion to the number of points he scored. The plan was put into practice. As predicted, players became proficient at the skills they needed to score points. Shooting was central, and players put a huge amount of effort into this particular aspect of the game. But one had to have the ball and get open before one could shoot, the players also worked on rebounding, stealing the ball, and ball handling. Those with the greatest combination of talent and personal motivation became the highest scorers and were the most highly rewarded. Most people looked …
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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 | 0.047 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.019 | 0.090 |
| Scholarly communication | 0.025 | 0.053 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.019 | 0.039 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".