Innovation in a crisis: rethinking conferences and scholarship in a pandemic and climate emergency
Bibliographic record
Abstract
It is a cliché of self-help advice that there are no problems, only opportunities. The rationale and actions of the BSHS in creating its Global Digital History of Science Festival may be a rare genuine confirmation of this mantra. The global COVID-19 pandemic of 2020 meant that the society's usual annual conference - like everyone else's - had to be cancelled. Once the society decided to go digital, we had a hundred days to organize and deliver our first online festival. In the hope that this will help, inspire and warn colleagues around the world who are also trying to move online, we here detail the considerations, conversations and thinking behind the organizing team's decisions.
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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 | 0.083 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.033 | 0.087 |
| Scholarly communication | 0.043 | 0.042 |
| Open science | 0.005 | 0.037 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".