Public Deliberation on Climate Change: Lessons from Alberta Climate Dialogue
Bibliographic record
Abstract
There exists in both academic and political circles a growing interest in public deliberation as an alternative to the sometimes adversarial and polarizing public engagement activities that result in the pitting of experts against lay people. Proponents of public deliberation claim that a more deliberative process can engage a diversity of participants in a more guided process that better balances expert knowledge and citizen inclusion. Such an approach holds particular promise where citizens and governments engage in discussions of the most complex and intractable issues like climate change. Given the host of challenges climate governance presents and the global consequences of our response to them, the experience and knowledge shared by Hanson and the contributors to Public Deliberation on Climate Change provide an important framework for advancing public conversations and processes on this and other wicked problems. The lessons contained in the volume were gained as a result of a five year multidisciplinary, community, university research project called Alberta Climate Dialogue (ABCD), which drew together scholars, practitioners, citizens, civil society members, and government officials from across Alberta at four public deliberations. By highlighting the value tensions and trade-offs and examining the impact that the design of the deliberations has on policy and the creation of conditions that encourage exchange, the contributors aim to build capacity within our institutions and society to find new ways to discuss and solve complex problems.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.027 | 0.019 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".