Bibliographic record
Abstract
This article explores the reasons behind Canada’s declining participation in United Nations peacekeeping operations. It proposes a decision-making model that explains how politicians assess opportunities to commit personnel to peacekeeping missions by balancing their policy objectives with the pressures of electoral politics. Emphasizing the importance of voters in political decision-making processes, it argues that participation in peacekeeping is dependent on three key factors: a belief in the value of peacekeeping in principle; a belief in the value of a given peacekeeping operation; and risk aversion in response to the potential costs of peacekeeping. Tracing Canada’s declining participation in peacekeeping operations since the 1990s, it particularly focuses on how this calculus has, in different ways, limited Canada’s involvement in peacekeeping under Stephen Harper’s Conservative government and Justin Trudeau’s Liberal government, arguing that the former undervalued peacekeeping as a means of obtaining its foreign policy objectives and as a feature of national identity, minimizing the perceived benefits of participation, while the latter has focused on the inherent risks of peacekeeping despite a professed commitment to peacekeeping in principle, maximizing the perceived costs of further personnel commitments. The decisions of successive Canadian governments have led to a free-rider problem in which Canada is willing to enjoy the benefits of peacekeeping but unwilling to bear the costs.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".