Researching in Times of Crisis: Toward <scp>Information‐Resilient</scp> Societies
Bibliographic record
Abstract
Abstract This panel features four experts in Library and Information Science (LIS) research who will present findings from major projects conducted during the COVID‐19 pandemic from national and international contexts. These include: a) a mixed‐methods study of virtual reference services in academic libraries during the pandemic's beginning, b) semi‐structured interviews with 29 global library leaders about library models that emerged in response to changes caused by the pandemic, c) a questionnaire survey of information professionals from the Association of Research Libraries (ARL) regarding linked data technologies, and d) a mixed‐methods approach to study newcomers and (mis)information during the pandemic including in depth interviews and content and sentiment analyses of social media platforms. The panelists will describe obstacles and challenges encountered during the pandemic, and efforts to overcame these. They will provide an overview of major findings and share research‐based implications for building and maintaining information‐resilient societies.
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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 | 0.037 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.024 | 0.026 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".