A trainee's perspective on scientific conferences
Bibliographic record
Abstract
Attending and presenting at conferences is a preeminent experience during scientific training. This article provides a trainee's perspective on strategies to promote trainee growth before, during and after scientific meetings, taking the initiatives implemented by the Canadian Society for Immunology (CSI) as an example. A foremost action was the establishment of the Trainee Engagement Committee (TEC) in 2020. The TEC members contribute to the annual symposia by participating in the local organizing committee meetings and organizing trainee-directed events. We propose actions that conference organizers can take to foster the development of the next generation of scientists. In addition, we offer advice to conference presenters on how to craft talks with trainees in mind and to attendees on how to maximize the quality and longevity of conference interactions. We hope this opinion piece evokes reflections and discussions among scientific societies, organizing committees, conference presenters and trainees alike.
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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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.028 | 0.015 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.016 | 0.027 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".