Unarmed civilian peacekeeping: What do Canadians think?
Bibliographic record
Abstract
Recent surveys have demonstrated that Canadians value Canada’s role as a peacekeeper and peacemaker in an international context. Additionally, research has demonstrated decreasing public support for Canada’s involvement in military combat interventions in other parts of the world. However, awareness and understanding of nonviolent alternatives appear to be lacking. This survey examines Canadian public’s awareness and understanding of unarmed civilian peacekeeping as an alternative to sending armed troops, and whether the public would support Canada in utilizing unarmed civilian peacekeepers (focusing on mediation, negotiation, relationship and peacebuilding activities) as part of its response to violent global conflicts. The results reveal that Canadians believe unarmed civilian peacekeeping would be more effective in tasks such as reducing human rights abuses, preventing further armed conflict and promoting lasting peace. Respondents also believe the practice would benefit Canada’s reputation as a peacemaker and leader. This paper concludes with recommendations for proponents and advocates of the incorporation of unarmed civilian peacekeeping into the official policy of the Canadian government.\n1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".