Evaluating deliberative participation from a social learning perspective: A case study of the 2012 National Energy Deliberative Polling in <scp>post‐Fukushima</scp> Japan
Bibliographic record
Abstract
Abstract Nuclear power has remained a hugely controversial energy technology since the 1970s and became particularly so after the 2011 Fukushima nuclear accident. Engaging citizens in making energy decisions have thus become an increasingly important governing approach to post‐Fukushima energy transitions in many countries. Deliberative participatory processes and learning through social interactions have been increasingly regarded as critical elements of effective public engagement. Yet, little is known about who learns what, how they learn, and what impacts learning has on nuclear governance. Even less is known about the contextual factors influencing social learning. Advancing the literature on nuclear governance, deliberative participation, and social learning, this paper proposes a learning‐oriented framework to evaluate the outcomes of deliberative participation in the context of nuclear governance. We apply this framework in a case study of a national deliberative poll (DP) on energy conducted in Japan in 2012. We critically examine the extent to which and how social learning occurs under the influence of pre‐existing government‐industry‐society relations as a key contextual factor. Mainly based on a qualitative analysis of transcribed materials from a two‐day deliberation over the DP involving 285 citizens, this study has three main findings. First, participating citizens of the DP were able to acquire all of the three orders of social learning through deliberative processes in the DP process. Second, the provision of multiple sources of information, access to diverse perspectives, and the availability of plenty of dialogic processes are identified as factors that were found to facilitate advancement toward higher orders of learning. Third, the “nuclear iron triangle”—a pro‐nuclear coalition—appeared to constrain social learning impacts in the wider socio‐political systems of nuclear governance in Japan.
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.
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 | 0.034 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| 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".