Locals in the Wasteland: The Non-Intended Side Effects of (In)Sensible Local Participatory Technology Development.
Bibliographic record
Abstract
Implementing high-level radioactive wastes programs on a territory remain a sensitive step for nuclear waste management organizations. At this stage, the preferred solution of radioactive wastes programs (geological disposal) and their technological developments become highly visible and this is why the new instruments and strategies such as participatory technology development (PDT) have been adopted to tackle the territorial development of geological disposal technology. Considering the coproduction between territory and program (Jasanoff 2004), this presentation focuses on two siting processes of radioactive wastes programs, in France and in Canada to analyze the influence of in(sensible) local participatory technology development strategy and the influence of invited critics (Wynne 2007) on the industrial project. Our results show that frameworks of the participatory technology development have been designed very differently (with or without publics). Consequently, two undesirable, mostly non-intended side effects of information local committees (non) integration have appeared. In France, a closed PDT transformed invited critics as non-invited one. In Canada, a weak engagement of publics has occurred with an opened PDT. In both case, new socio-technical adjustments have occurred in those programs.
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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 | 0.026 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".