Ethical policy analysis in an age of risk, uncertainty, and futurity
Bibliographic record
Abstract
Once dominated by approaches based on positivist assumptions, the field of policy analysis has diversified in recent times. Policy analysis now includes numerous perspectives on the processes of policy formulation, implementation, and evaluation. Many of these approaches derive from self-consciously defined schools of ethics. This dissertation surveys three generalized approaches to ethical policy analysis and evaluates them in light of moral dilemmas arising in the case of nuclear waste management policy in Canada. Its central argument is that an adequate approach to ethical policy analysis contains the philosophical tools necessary to address the moral problems of defining risk and understanding safety, identifying obligations to both existing and future generations, and conceptualizing legitimacy-conferring policy processes. Neither welfare utilitarianism nor modern deontology is sufficiently equipped, each containing philosophical elements of the good that beg further determination in actual policy contexts. Only the deliberative/discursive approach contains convincing conceptions of justice and the good, as well as a convincing conception of legitimacy, that provide for the justifiable resolution of debates about the moral foundations of public policy. Responding to challenges in the case of nuclear waste management in ways more comprehensive and more justifiable than both utilitarianism and deontology, discursive policy analysis promises to be an effective approach in other cases associated with risk, uncertainty, and futurity.
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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 | 0.033 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.063 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".