Data in the Time of COVID-19: How Data Library Professionals Helped Combat the Pandemic
Bibliographic record
Abstract
As the world struggled to respond to the COVID-19 pandemic, researchers worked around the clock to understand what was going on, medically, socially, and economically. At the same time, usual research processes were disrupted: campuses were closed and normal government data collection and dissemination went haywire. Data professionals in academic libraries sprang into action to help. They shared resources, developed workshops, helped find alternative methods of carrying out research, and found ways of coping with the influx of COVID-related data. Social crises are fought on the front lines by medical professionals and service providers, but they are also fought with research, with information, with data. Libraries are at the nexus of information and communication and library professionals were able to play an important supporting role in helping researchers combat the effects of the pandemic.
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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 | 0.087 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.043 | 0.025 |
| Scholarly communication | 0.049 | 0.056 |
| Open science | 0.005 | 0.036 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".