Bibliographic record
Abstract
Like most contributions to the field of security studies, this book was written in reaction to events.We convened a number of experts to take part in a workshop at the Centre for International and Defence Policy to consider several significant questions.First, Canada's withdrawal from the Afghanistan War prompted us to ask: what's next?Second, under what conditions would Canada commit to another sizable deployment in the future?Based on our assessment, which sought to bridge the civilian-military divide, the answer was unclear and certainly warranted more research.The contributors to this edited volume, a diverse group of university professors, defence scientists, senior military officers from several different countries, and former senior Canadian public servants, rose to the challenge and produced a thought-provoking collection.In answering questions about Going to War, the volume reflects a diversity of views and perspectives.Moreover, we have witnessed a number of the authors' predictions play out since the time of writing.One of these predictions is that Canadian interventions post-Afghanistan will be small in size, limited in scope, and far from the minds of everyday Canadians.Gone are the Red Fridays that typified the home front of the last decade or so.Starting in the mid-2000s, local groups started encouraging people to wear red clothing on Fridays as a sign of support for Canadian soldiers.Red Fridays caught on and -particularly in communities close to Canadian military bases -people started wearing red T-shirts on Fridays each week.Moreover, the "Highway of Heroes" -the route from the airforce base in Trenton to the Ontario Coroner's Office in downtown Toronto -is simply a series of signs on an otherwise jam-packed limited-access highway.Canada's
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.325 | 0.185 |
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".