Deliberating Future Issues: Minipublics and Salmon Genomics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it