Bibliographic record
Abstract
Abstract This article explores the attitudes of Canadian officials towards international conflict mediation and towards the potential for greater official Canadian involvement in the field. The study is based on extensive interviews with Canadian officials who have been involved in mediation at various points over a 20-year period. It finds that Canada, and particularly the Department of Foreign Affairs and International Trade (DFAIT), has taken a largely ad hoc approach to its involvement in the field. Prior to the initiation of this study, there had been no attempt to develop an institutional capacity in this field within DFAIT or to keep track of the personnel involved in such experiences, much less to develop a trained cadre of such individuals. This stands in contrast to the efforts of countries that have prioritized mediation as a foreign policy activity, such as some Scandinavian countries and Switzerland. Many of those interviewed pointed to these countries as potential models for Canada in this field, but it became apparent in discussions that most of those interviewees were not necessarily well-informed as to what these countries have done; there was just a general sense that these countries do it well and that Canada could learn from them. Moreover, none of the interviewees demonstrated significant familiarity with the vast literature on mediation. Those interviewed made recommendations as to how Canada might develop its official mediation capacities so as to play a more active and focused role in this field.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.029 | 0.015 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".