Anticipating the Impacts of Integrating Disruptive Technologies from Societal Dialogues, A Promising Tool?
Bibliographic record
Abstract
The recent history of technological development has taught us that it is essential to consider a technology's social readiness level before considering its integration into society. While we have relied heavily from the outset on local participation to prepare for this integration, some experiences have been more positive than others. Two examples, nanotechnology in France and the Montreal Declaration in Quebec, can serve as benchmarks for the necessary conversation around current advances in future quantum technologies, even before they are developed. In the context of the creation of Sherbrooke's Quantum Innovation Zone, DistriQ, the “Quantum Dialogues” project launched from the Institut quantique (Université de Sherbrooke) aims to foster collaborations between the research community, entrepreneurs and society in an integrated dialogue-based approach. This article focuses on the first phase of this project: the application of a specific participatory practice, the Transformative Scenario Planning, from Kahane [1], to the development of quantum technologies at multiple levels of governance structures.
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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 | 0.035 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.025 | 0.042 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".