Troutville: Where People Discuss Fairness Issues
Bibliographic record
Abstract
Context. Public engagement efforts in health policy have posed many value-laden questions, yet those that appreciate the complexity and diversity of the concept of health equity are rare. We introduce the Fairness Dialogues, a new method for deliberating health equity among the general public. We provide its theoretical underpinning and present its empirical illustration and qualitative assessment. Methods. Primarily informed by the scholarship of deliberation, we designed the Fairness Dialogues, featured by reason-giving and inclusive group deliberation using a hypothetical scenario (the town of Troutville) that presents carefully designed, simple, open-ended cases focusing on a chosen equity and fairness issue. To assess whether the Fairness Dialogues encourages reflective views, we conducted a qualitative investigation by focusing on fairness and unfairness of inequalities in life expectancy. Findings. Our results revealed the complex intuitions that people have and their curiosity, patience, and willingness to scrutinize them in-depth through a small group dialogue. Intuitions shared by our study participants are similar to those presented in the scholarly philosophical literature. Conclusions. The Fairness Dialogues is a promising method to incorporate the public’s views into policy-making involving value judgment and to develop the capacity of the public to discuss value-laden questions in a reflective and inclusive manner.
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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 | 0.042 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.025 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.019 | 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".