JLSC Board Editorial 2021
Bibliographic record
Abstract
It hardly needs to be said that 2020 was a difficult year for the world. COVID-19 has infected over 120 million people and killed over 2 million as of March 2021 (Johns Hopkins). At the same time, police violence against people of color continues, even as communities engage in long-overdue reckoning initiatives. Across the globe, researchers, governments, and communities needed quick, open, up-to-date information on testing for, treating, and preventing COVID-19. Our increased dependence on technology during lockdowns provided some with safety and continuity, while others experienced the widening of the digital divide. There is no greater urgency than the work of identifying and addressing issues of inequality and lack of equity and inclusivity.Although the results remain to be seen, the field of scholarly communications experienced disruption in 2020. The editorials below discuss these recent changes and imagine what could come out of the pandemic. We hope that these reflections invite conversation and action.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.133 | 0.089 |
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".