Framing and power in public deliberation with climate change: Critical reflections on the role of deliberative practitioners
Bibliographic record
Abstract
Drawing on the experiences of a deliberative practitioner and critical social scientist involved in the planning, production and implementation of a deliberative initiative on climate change, this paper reflects on nuances of framing and power in practical settings. Decisions about framing, some of them more conscious than others, influence the process of opinion formation among participants as well as the outcomes of the deliberation. Framing enacts power through the selection of deliberative approaches, the viewpoints that are admitted into the procedure, the alternatives that are defined, as well as the solutions that are ultimately proposed. Grounded in reflexivity as a methodological approach, the goal of this analysis is to make the democratization of public responses to climate change more reflexive and open to transformative learning at individual and institutional levels.
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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 | 0.172 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.027 | 0.191 |
| Scholarly communication | 0.029 | 0.039 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.017 | 0.027 |
| Insufficient payload (model declined to judge) | 0.003 | 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".