Bibliographic record
Abstract
Science fiction movie fans will already be familiar with one possible future for emergency medicine education and training.In the 1999 film The Matrix (Warner Bros. ® Entertainment) Keanu Reeves's character Neo simply connects to a computer via a port in the back of his head and receives a 'direct download' of knowledge and skills ranging from martial arts and bullet dodging to languages (enabling him to save the world, obviously).The experience appears briefly painful but incredibly efficient.This might be closer to reality than we think.1,2 The approach taken in The Matrix is the logical extension of medical education and training methods over the past 50 years.As the apprenticeship model used in the preceding 800 years has fallen out of vogue, medical educators have sought to codify and collate their body of knowledge.The training task has been the transmission of this knowledge and skills, with a passing interest in the acquisition of appropriate attitudes and behaviours.Traditional lectures and textbook-based education have been enhanced by advanced curricular design, explicit learning outcomes and psychometrically reliable assessment.Technology, including Social Media, has further improved the effectiveness and efficiency of knowledge transmission.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".