How To Find A Balance Between Bibliometric And Societal Impact In Academia.
Bibliographic record
Abstract
Interview with Kai Chan for Elephant in the Lab – the blog-journal on science policy. http://elephantinthelab.org/ Kai Chan is a full professor in the Institute for Resources, Environment and Sustainability at University of British Columbia. He is an interdisciplinary, problem-oriented sustainability scientist, trained in ecology, policy, and ethics from Princeton and Stanford Universities. Kai is a Leopold Leadership Program fellow, a Coordinating Lead Author of the IPBES Global Assessment, a member of the Royal Society of Canada’s College of New Scholar, Artists and Scientists, a director on the board of the North American section of the Society for Conservation Biology, a member of the Global Young Academy, a senior fellow of the Environmental Leadership Program, and (in 2012) the Fulbright Canada Visiting Research Chair at the University of California, Santa Barbara.
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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 | 0.120 | 0.395 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.035 | 0.050 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.025 | 0.048 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".