Enhancing Citizen Engagement in Cancer Screening Through Deliberative Democracy
Bibliographic record
Abstract
Cancer screening is widely practiced and participation is promoted by various social, technical, and commercial drivers, but there are growing concerns about the emerging harms, risks, and costs of cancer screening. Deliberative democracy methods engage citizens in dialogue on substantial and complex problems: especially when evidence and values are important and people need time to understand and consider the relevant issues. Information derived from such deliberations can provide important guidance to cancer screening policies: citizens' values are made explicit, revealing what really matters to people and why. Policy makers can see what informed, rather than uninformed, citizens would decide on the provision of services and information on cancer screening. Caveats can be elicited to guide changes to existing policies and practices. Policies that take account of citizens' opinions through a deliberative democracy process can be considered more legitimate, justifiable, and feasible than those that don't.
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 | 0.063 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".