Bibliographic record
Abstract
This paper explores the plausibility of a rather novel solution to the problem of domestic terrorist threats: might the risk posed by individuals from communities that are thought to be prone to acts of political violence and terrorism be mitigated by recruiting to the military members of communities that public and political discourse has deemed a fifth pillar? The military presents a stable, well-paying career to individuals marginalized by their ethno-religious identity that have to this point been grossly under-represented in the Canadian Armed Forces. It can also bring members of these communities into a closer relationship with the state and mainstream society that will foster allegiance, combat alienation, and stifle the desire to commit violence against Canada and its citizens. Two implications follow for the Canadian Armed Forces from the explanation of the Toronto 18 as a non-peaceful node of an identity-based network. One is that the institution should be cautious not to fall into the trap of a populist vernacular reification of identity that might inadvertently further the community’s collective alienation by overtly appealing to “Arabs” or “Muslims”. The other is that the military may do well to focus on the equality of treatment within their institution rather than material benefits when attempting to extend an olive branch to Canada’s marginalized communities, instead making an emotional appeal based on the common good.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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".