A Tip to Pre-Med Students: Don't Put All Your Eggs in the Science Beaker
Bibliographic record
Abstract
Every year in Canada, over 10 000 students apply to medical schools across the country (1). Each of these applicants has a unique story to tell about why they’re perusing medicine as a career, but all of them are trying to figure out the best academic pathway into the program. Arguably, it is the academic pathway that will provide them with the most successful career in medicine that should be of more importance. Undergraduate studies are a grossly underutilized resource by many pre-med students who seem content focusing solely on Science when in reality, the majority of what’s taught has little to no utility in their prospective futures.
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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.019 | 0.034 |
| Insufficient payload (model declined to judge) | 0.076 | 0.043 |
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".