Mitigating the impact of conference and travel cancellations on researchers’ futures
Bibliographic record
Abstract
The need to protect public health during the current COVID-19 pandemic has necessitated conference cancellations on an unprecedented scale. As the scientific community adapts to new working conditions, it is important to recognize that some of our actions may disproportionately affect early-career researchers and scientists from countries with limited research funding. We encourage all conference organizers, funders and institutions who are able to do so to consider how they can mitigate the unintended consequences of conference and travel cancellations and we provide seven recommendations for how this could be achieved. The proposed solutions may also offer long-term benefits for those who normally cannot attend conferences, and thus lead to a more equitable future for generations of researchers.
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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 | 0.057 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.044 | 0.006 |
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".