Deliberating Future Issues: Minipublics and Salmon Genomics
Bibliographic record
Abstract
In this paper we are interested a class of issues that are especially difficult to address through public engagement processes. These are issues which should (or must) be addressed in the current period but have associated costs, benefits, and impacts that are concentrated in the future. These issues – which might be called ‘future issues’ – are difficult to manage democratically because any public opinions that might help guide policy decisions have not yet developed. At the same time, governments and administrative agencies are often compelled to act before the full implications of these issues are evident and before potentially affected publics are formed and aware of the implications or consequences of these developments. At best, governments and administrators can try to facilitate positive developments or prevent negative outcomes by anticipating potential concerns or conflicts associated with future issues and addressing these in the current period. We argue that small deliberative forums that combine random-selection, education and deliberation are a practical solution to this dilemma. These small forums – or minipublics – can be used to simulate discursive opinions on subjects that have not, or have not yet become topics of widespread public discourses. Our analysis is based on data from a minipublic on salmon genomics that was conducted in November 2008 by the Centre for Applied Ethics at the University of British Columbia. We argue that participating in deliberative events like this one can help citizens develop substantive opinions on technologically and temporally complex issues. We also argue that minipublics can be used to develop anticipatory maps of collectively sanctioned recommendations and discursively developed concerns or considerations. Minipublics on future issues can offer policy makers important insights into the likely parameters of public debates that have not – or have not yet – occurred.
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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 | 0.097 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".