Bibliographic record
Abstract
In decision-making about epidemic management, experts and policymakers in governmental institutions face the challenge of balancing numerous values, needs and interest. These decisions are complex, and have to be made within a context of uncertainty, public unrest and limited time. Despite the potential value of public engagement, the perspectives of the publics are limitedly integrated in decisions about epidemic management. Publics possess unique experiences, concerns, needs and ideas that can aid in the complex trade-offs at certain times in epidemics. Furthermore, engaging the publics can enhance their understanding of decision-making processes, and possibly even garner public support for these decisions and trust in decision-making institutions. Up until now, publics have for example supported response activities in epidemic management such as surveillance, improvement of communication campaigns or the execution of restriction measures. Yet, the structural integration of public values, needs and experiences into the development of epidemic management strategies has not been realized. This raises uncertainties about the feasibility and methodology of incorporating public engagement in epidemic management. The primary objective of this thesis was to explore whether and how public engagement can be integrated into decision-making about epidemic management, using the COVID-19 epidemic as a case.
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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".