Deliberation for Reconciliation in Divided Societies
Bibliographic record
Abstract
Engaging with the literature on deliberative democracy, this article contends that in the context of ethnic group hostilities, deliberative processes where participants have a genuine opportunity to communicate and ‘hear the other side’ can be a way for inter-group dialogue and reconciliation. Separating the deliberative process into three distinct moments, it offers a framework for understanding how unequal and conflicting parties may be brought together to deliberate, how to grasp the micro-politics of deliberation, and to understand the diffusion mechanisms that bring society back in. The approach we propose aims to bridge the normative-macro and the experimental-micro accounts of deliberation in order to focus on non-ideal real-life contexts and to offer ‘deliberative lenses’ to study the (rare) cases of deliberative inter-ethnic reconciliation. The approach and the three moments are illustrated by the deliberative turn taken to resolve a conflict between the Innu communities, the Quebec government and the local non-Innu in Saguenay-Lac-Saint Jean.
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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 | 0.034 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.061 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.005 | 0.004 |
| 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".