What's Your “Position” on Nuclear Power? An Exploration of Conflict in Stakeholder Participation for Decision‐making about Risky Technologies
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
Abstract One of the exigent issues concerning stakeholder engagement in decision‐making for nuclear power generation facilities is the manner in which conflict is acknowledged and managed. In order to manage conflict effectively, one must understand the full variability of positions on certain issues, and from perspectives of diverse stakeholders. Research to‐date on risk perception and resultant conflict has been conducted almost exclusively from the perspective of the public. This paper utilizes grounded theory methodology combined with a case study approach to explore the decision‐making processes for the re‐licensing of two local nuclear power generation facilities; Pickering ‘A’ and Bruce ‘A’ from the perspective of the nuclear industry, the Canadian Nuclear Safety Commission, government, Non‐Governmental Organizations, scientists, and public. Situational analysis is a tool utilized to generate 7 positional maps which display the diversity of viewpoints on major contested issues such as environmental, technical, and financial risk, availability of information, and satisfaction with the process for stakeholder engagement. The paper concludes that conflict can be both negative and positive, and recommends the best way to reduce the potential for conflict is by actively engaging stakeholders in facilitated discussions with ground rules for conduct of all participants, and conducting discussions over the long‐term and well before (and after) legislated public consultations take place. The results of this study provide a useful framework for future quantitative exploration of the issues illustrated in each of the 7 positional maps.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| 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