Contribution des chercheuses et des chercheurs aux politiques publiques : perspectives sur les enjeux éthiques
Bibliographic record
Abstract
Technologies for aging are emerging as promising solutions to meet the needs of the elderly. In Quebec, university researchers working in this field can play an important role in promoting changes in public policy aimed at facilitating the integration of these technologies into society. Indeed, scientific actors are increasingly encouraged to explore the political sphere and increase the social impact of their work. However, exercising this role entails ethical, personal and practical challenges for researchers regarding their choice of contribution and the methods to be employed. This doctoral thesis in practical philosophy explores the ethical issues involved in researchers' contributions to public policy on technologies for aging. The methodological approach adopted in this research is qualitative and empirical, based on a multiple-case study. We conducted a documentary observation of Quebec's public policies on technologies for aging, followed by 18 semi-structured interviews with scientific and political actors. Our results show that researchers' contribution to public policy varies according to their motivations, skills and disciplinary field, ranging from active engagement in public debates to indirect involvement via teaching. Institutional initiatives, such as training opportunities on the skills essential for political influence and recognition of these contributions, are needed to strengthen and enhance this role. The results also show that contribution can be thought of collectively, through research groups and laboratories. Finally, this thesis contributes to our understanding of the science-policy interface from an ethical point of view. We also propose a reflexive analysis grid for researchers, with a view to integrating ethical reflection into their practices aimed at contributing to public policy.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.036 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.018 | 0.044 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".