Early-career researchers: an interview with Erika Eliason
Bibliographic record
Abstract
Erika Eliason is an Assistant Professor at University of California, Santa Barbara, USA, where she studies ecological and evolutionary physiology. She received her Bachelor's degree at Simon Fraser University, Canada, in 2003 before completing her MSc and PhD with Tony Farrell at the University of British Columbia, Canada. She then completed postdocs with Frank Seebacher at the University of Sydney, Steve Cooke at Carleton University, Canada, and Scott Hinch at the University of British Columbia, Canada. Erika gave the Cameron Award Lecture at the annual Canadian Society of Zoologists meeting in 2013.
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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 | 0.031 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.014 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.009 | 0.030 |
| Insufficient payload (model declined to judge) | 0.004 | 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".