Bibliographic record
Abstract
Greetings to all as we embark upon the last weeks of 2017! This fall has been a busy time for JCIPE in many different ways. At the end of September, thirteen JCIPE staff, faculty and students attended the Collaborative Practice conference held in Banff, Alberta, Canada. JCIPE delivered one invited pre-conference workshop and five peer-reviewed, accepted live presentations focusing on a wide range of our activities including Hotspotting, the Health Mentors Program, the Jefferson Teamwork Observation Guide (JTOG) and programmatic sustainability. We learned a lot relative to new program ideas, assessment suggestions and faculty development - just to name a few things garnered while in Banff that we're excited to bring back to Jefferson. We are currently beginning preparations for our sixth hosted conference, 2018 Interprofessional Care for the 21st Century that will take place on Jefferson's campus on October 26 and 27, 2018. Save the date!
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.169 | 0.149 |
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".