Cengagement des citoyens: Une alternative pour le renouvelle‐ment des relations entre les Autochtones et les non‐Autochtones
Bibliographic record
Abstract
Sommaire: L'engagement des citoyens dans la formulation des politiques publiques pose de nombreux défis. Certains secteurs de la société canadienne les révèlent avec une acuité toute particulière et les questions relatives aux relations entre les Autochtones et les non‐Autochtones en sont un exemple. Signalons entre autres que tout projet d'engagement doit d'abord s'appliquer à favoriser un rapprochement entre les communautés autochtones et non‐autochtones. Ce texte présente les enseignements tirés d'une expérience d'engagement menée au Nouveau‐Brunswick par une équipe d'apprentissage formée de fonctionnaires fédéraux et provinciaux, de citoyens autochtones et non‐autochtones et d'universitaires. On y propose un modèle original inspiré d'expériences menées dans d'autres secteurs d'activité au Canada et reposant sur cinq conditions: un climat de confiance, une information accessible et crédible, l'existence de points communs, un dialogue sur les valeurs et les convictions et un processus souple. Cet article aborde quelques‐unes des grandes questions actuelles: gouvemance, démocratie, innovation, diversité, cohésion sociale, culture et valeurs. Abstract: The engagement of citizens in government decision‐making poses multiple challenges. Some issues in Canadian society, such as relations between aboriginal and non‐aboriginal people, illustrate these challenges in a particularly evident manner. For example, any commitment initiative involving the aboriginal and non‐aboriginal communities must be designed first to promote reconciliation. This article outlines the lessons learned from a commitment experiment conducted in New Brunswick by a learning team made up of federal and provincial public servants, aboriginal and non‐aboriginal citizens, and academic staff. It offers an original model inspired by experiments conducted in other activity sectors in Canada and based on five criteria: climate of trust, accessible and credible information, focus on commonality, dialogue on values and convictions, and flexible process. This article addresses some of the major current issues: governance, democracy, innovation, diversity, social cohesion, culture and values.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".