Alliance Value and Status Enhancement: Canada's Disproportionate Military Burden Sharing in Afghanistan
Bibliographic record
Abstract
In choosing not to free ride while the United States was in Afghanistan, second‐tier powers like Canada challenge conventional accounts of military burden sharing. To elucidate the puzzle posed by excessive military contributions, the article considers two explanatory factors: alliance value and status seeking. We pair them with insights on elite consensus to explain Canada's desire to have a large role in the war in Afghanistan. We argue that it is expected to support U.S.‐led military operations and contributes more than its relative power if it seeks recognition of an elevated status by valued alliance members. Absent elite consensus, however, state executives may have difficulty implementing their status enhancement objectives. We further demonstrate the value of our reasoning by confronting it with the ever‐increasing factor of threat perceptions. We conclude by stressing that ideational motivations for intra‐alliance burden sharing pose a serious challenge to conventional accounts of state contributions to U.S.‐led military interventions. Related Articles Haar , Roberta . 2015 . “” Politics & Policy 43 (): 287 ‐ 314 . http://onlinelibrary.wiley.com/doi/10.1111/polp.12111/abstract Dumbrell , John . 2006 . “.” Politics & Policy 34 (): 452 ‐ 472 . http://onlinelibrary.wiley.com/doi/10.1111/j.1747-1346.2006.00021.x/abstract Walsh , James I . 2005 . “.” Politics & Policy 33 (): 642 ‐ 670 . http://onlinelibrary.wiley.com/doi/10.1111/j.1747-1346.2005.tb00217.x/full Related Media n.d. “Kandahar Journals.” http://www.cbc.ca/documentarychannel/docs/kandahar-journals n.d. http://www.wagingpeacefilm.com/Home.html 2017. “Out of Afghanistan ‐ Canada's 12‐Year War.” http://www.cbc.ca/news/world/afghanistan
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".