Bibliographic record
Abstract
The Vancouver Olympics reveal stark differences between the worlds of sports and science. In both, young people from around the world try to surpass all previous accomplishments in pursuit of world records or scientific discoveries. Selected entirely on merit, athletes receive honor just for participating in the games, spurring the next generation of young people in each nation to excel. And as star athletes age, they often support their sport in other ways, serving as advocates, mentors, or coaches. In contrast, in too many nations, the selection and promotion processes in science involve considerations other than merit. Senior scientists receive most of the resources available for scientific research, and young scientists rarely receive societal recognition for their work. This situation is growing worse as life expectancies and retirement ages increase, along with the average age for attaining scientific independence. * Perhaps as one consequence, science is typically not a top career choice. How many exceptional scientists around the world thereby go unrecognized, their talents allowed to wither away untapped? In an attempt to reverse such trends, a nascent “young national academies” movement has begun across the globe, and a new international group has recently been established to promote this cause.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".