Newsletter Fall 2019: Clinician Investigator Trainee Association of Canada (CITAC)
Bibliographic record
Abstract
A message from Elina Cook (President): Demystifying and promoting the MD-PhD/MSc world—our progress Clinician Investigator Trainee Association of Canada (CITAC) seeks to promote, support and advocate for trainees whose goal is to become physician/ clinician/surgeon investigators. These include trainees of MD-PhD/MSc programs and Clinician Investigator Programs (CIP), who are preparing themselves to succeed in the overlapping world of medicine and research. Thanks to the wealth of talent, skill and motivation of the CITAC leadership this year, we are delivering new opportunities to these trainees in a number of ways: 1) developing international partnerships and initiatives; 2) revitalizing the Annual General Meeting (AGM); 3) advocating for clinician/physician/surgeon-scientist trainee support among influencers and policy makers; and 4) collecting data on the academic “health” of our training programs and trainees across Canada.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.019 | 0.011 |
| Insufficient payload (model declined to judge) | 0.134 | 0.073 |
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".