Conference cancelled: The equitable flip side of the academic surgery coin
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) has converted the world to a “new normal” during which nothing is what it was before. By July 2020, over ten million people had been confirmed with COVID-19 and over 500,000 people have died.1 Understandably, public health measures put a halt on public events at the start of the pandemic. Around the world, mass gatherings and academic conferences have been cancelled; others have found innovative means to shift virtually. Previous outbreaks have all resulted in some form of social reform for the better: the bubonic plague improved worker conditions, cholera outbreaks improved water sanitation, and HIV/AIDS led to improved community-based health education--now, COVID-19 could bring an unexpected silver lining: a paradigm shift in the way we exchange academic information and hold conferences into the virtual world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.068 | 0.255 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.017 | 0.026 |
| Insufficient payload (model declined to judge) | 0.111 | 0.025 |
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".