Bibliographic record
Abstract
Exploring how and why communication breakdowns occur during pandemics and world disasters, this book offers solutions for improving communication and managing future public health crises. A compilation of evidence-based lessons learned, this book shows how to effectively convey critical lifesaving information during a pandemic. It assesses how trust in leaders and governments during a public health crisis is formed and the impact this has on how information is perceived by the public. Using the COVID-19 pandemic as a case study, the book demonstrates how informative policy decisions and health risk messages can be better communicated for the handling of future pandemics. At a macro-level, the book looks at issues concerning situational awareness, how different countries managed or mismanaged the pandemic, and the lessons readers can learn from those occurrences. At a micro-level, it examines individual differences in public health message perceptions and corresponding actions taken or not taken. An interdisciplinary critique of the delivery and reception of messages during global disasters, this text is suitable for undergraduate and graduate courses in Communication Studies, Health Communication, Risk Communication and Public Health, Psychology, Sociology, and Disaster Management.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.017 |
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".