(De) Securitizing the Arctic? Functional actors and the shaping of Canadian Arctic security policy.
Bibliographic record
Abstract
This dissertation examines the development of modern Arctic security policy. It is a longitudinal investigation that begins in 1985 when Canada had an Arctic policy of “ad hocery” and ends in 2010 with the completion of an integrated policy. It investigates how the threat perceptions and policy prescriptions of various domestic actors were transmitted into government policy, moving some conceptualizations of security up or to the top of the agenda whilst moving others down or off of it. Second generation securitization theory is systematically applied to a series of exceptional case studies that best track the change over time in Arctic security policy. A mixed methodology of process tracing and discourse analysis interrogate the creation and changing of context, and how context was critical in setting the conditions for shaping policy. The dissertation finds that context matters in the securitization process, largely being created by securitization theory’s undertheorized functional actor. These actors provide policy options for those with political power to securitize into government policy. The prescriptions these actors offered were increasingly complex, stretching across the breadth and depth of security over time. This dissertation tells a story that comes full circle, beginning and ending with Canada’s effort to fold the military into its developing Arctic security policy.
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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 | 0.003 | 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 | 0.026 | 0.020 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".