How to connect academics around the globe by organizing an asynchronous virtual unconference
Bibliographic record
Abstract
Many conferences and in-person meetings have transitioned to virtual platforms in response to the COVID-19 pandemic. Here, we share strategies and lessons learned from organizing an international virtual unconventional conference, or ‘unconference’. The event focused on how early career researchers can advocate for systemic improvements in scientific publishing and research culture. The virtual unconference had three main components: (1) a virtual networking event, (2) asynchronous virtual brainstorming, and (3) a virtual open space, where participants could join or lead in-depth discussions. The unconference format was participant-driven and encouraged dialogue and collaboration between 54 attendees from 20 countries on six continents. Virtual brainstorming allowed participants to contribute to discussions at times that were convenient for them. Activity was consistently high throughout the 48 hours of virtual brainstorming and continued into the next day. The results of these discussions are collaboratively summarized in a paper entitled Empowering Early Career Researchers to Improve Science , co-authored by the unconference participants . We hope that this method report will help others to organize asynchronous virtual unconferences, while also providing new strategies for participant-driven activities that could be integrated into conventional virtual conferences.
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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 | 0.026 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.016 |
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".