Out of COVID, a conference: lessons from creating a new, free, and entirely virtual academic meeting amidst a pandemic
Bibliographic record
Abstract
One potentially positive result of the movement towards virtual academic meetings, accelerated by the COVID-19 pandemic, is increased democratization of the creation, implementation, and attendance of academic conferences. Here we describe an early ``proof of principle" of this democratizing power via our experience organizing the Canadian Computational Neuroscience Spotlight (CCNS), a free two-day virtual meeting that was built entirely amidst the pandemic using only virtual tools. This meeting was unique not just in the challenges faced by creating a conference during a pandemic: it was crafted entirely by early-career researchers and without any sponsors or partners, advertised primarily using social media and ``word of mouth", and designed specifically to highlight and engage trainees. It is our hope that this success will encourage other young scientists to embrace the challenge of organizing their own unique conferences facilitated by the benefits of the digital space, further democratizing the landscape of academic meetings.
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.010 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.005 | 0.009 |
| 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".