Using remote seminars to teach animal behavior
Bibliographic record
Abstract
Abstract In response to the COVID‐19 crisis, numerous academic conferences and seminars were moved online. Some remote (online) seminars have the aim to be maintained permanently after the pandemic, offering weekly opportunities for scientists, postdocs, and students to learn about research and to improve global networking. Remote seminars are a good option to promote inclusion and diversity, allowing students worldwide to participate and to interact with researchers from a broad cultural and ethnic background. Capitalizing on our experience with the ongoing International Remote Seminar on Frontiers in Social Evolution (FINE), we propose four teaching tools that can be integrated into undergraduate and graduate courses and that can be adapted for use with most remote seminar series. We make recommendations for the use of: (i) Certified remote seminar attendance. (ii) Relevant articles from the primary literature. (iii) Teaching slides, and (iv) Recorded seminars. Our aims are to promote and facilitate the use of the proposed teaching tools in Animal Behavior and related courses, and to encourage other remote seminar organizers to make teaching tools available.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.008 |
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".