Readily Available Digital Technologies in the Age of Pandemics
Bibliographic record
Abstract
Digital technologies and information systems have played a pivotal role during the past SARS pandemic and continue to assist with the recovery process during the ongoing COVID-19 pandemic. Nonetheless, the technologies themselves have advanced significantly and allow ready access and ease of use to individuals, organizations, and communities. We focus on a set of such technologies – Readily Available Digital Technologies (RADT) – and show how they assist during various phases of management of the ongoing crisis. We utilize an existing crisis management framework and emphasize the role and impact of RADTs. Furthermore, we extend the crisis management framework to include a resilience phase and explore examples from extant academic and practitioner literature to demonstrate its applicability in the current context. We invite future researchers to build further on our exploratory framework and highlight its potential contributions.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".