The 'Research Derby'" A pressure cooker for creative and collaborative science
Bibliographic record
Abstract
Ecology and evolution research benefits when scientists engage in meaningful collaborations. However, making time for such efforts is difficult, particularly for early-career graduate students who are often focused on an independent and self-driven research program. Here, we introduce the concept of the Research Derby, a collaborative and semi-competitive workshop where teams are given 24 hours to complete a research project. This ‘pressure-cooker’ environment is designed to give scientists a fun and short-term opportunity to conduct research outside their primary field, promote skills exchange within the research group, and ultimately produce high-quality scientific publications. In this manuscript we outline the goals of the Research Derby, explain how to set up such an event, and recount our experiences running a Derby within our research group at Simon Fraser University, Burnaby, B.C., Canada. We argue that Research Derbies have the potential to achieve creative and collaborative high-impact science, and are a fun and productive research activity.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 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".