Newsletter Fall 2020: Clinician Investigator Trainee Association of Canada (CITAC)
Bibliographic record
Abstract
Message from the CITAC president To say that 2020 has been an unprecedented year is an understatement. The coronavirus disease 2019 (COVID-19) global pandemic and the major societal awakening on racial equity and justice have led us to reflect on our direction, goals and mission. Thanks to our talented and dedicated executive team, we were able to pivot our efforts and adapt to the changing landscape of research and advocacy. In April, we provided our members with a list of resources to help facilitate a smooth transition to working from home. In June, we published Clinician Investigator Trainee Association of Canada’s (CITAC) press release on our role in combating anti-Black discrimination and racial injustice and have outlined specific advocacy efforts that we will be committing to over the next years (the full statement can be found on our website, https://www.citac-accfc.org). Tina B. Marvasti, MSc, MD/PhD Candidate, Class of 2022, Faculty of Medicine, University of Toronto, President, Clinician Investigator Trainee Association of Canada (CITAC)
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.016 | 0.011 |
| Insufficient payload (model declined to judge) | 0.211 | 0.092 |
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".