Bibliographic record
Abstract
’m typing this sat in the beautiful Banff Springs hotel in Canada where I’ve spent the last couple days at #KTNConf Inspiring Change. Organised and hosted by the Health Research Transfer Network of Alberta (RTNA), this conference has focused on the role of knowledge transfer in inspiring change. Rarely have I had the privilege to attend such a warm and friendly, insightful and broad ranging conference. Today alone I heard about maternal health, homelessness and mental health, built environment and early years, Well Doc? initiative on physician stress, storytelling and aboriginal trauma, enhanced surgery and recovery, parent narratives of life in a neonatal intensive care unit and mathematics teaching for children with feotal alcohol spectrum disorder. All in one day, together with great opportunities for networking and poster presentations, lightening talks and a very entertaining gameshow after dinner of Knowledge Transfer Jeopardy. It’s been a seriously great day, I’ve got that mid conference buzz, brain ache and exhaustion all rolled into one.
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 | 0.168 | 0.271 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.010 | 0.047 |
| Scholarly communication | 0.027 | 0.027 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.022 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".