Security Inequalities in North America: Reassessing Regional Security Complex Theory
Bibliographic record
Abstract
This article re-evaluates earlier work done by the authors on Regional Security Complex Theory (RSCT) in North America, using sectoral analysis initially developed by Buzan and Waever, but also adding the variables of institutions, identity, and interests. These variables are assessed qualitatively in the contemporary context on how they currently impress upon the process of securitization within sectoral relations between Canada, Mexico, and the United States. The article reviews the movement from bilateral security relations between these states to the development of a trilateral response to regional security challenges post- 9/11. It further addresses the present period and what appears to be a security process derailed by recent political changes and security inequalities, heightened by the election of Donald Trump in 2016. The article argues that while these three states initially evinced a convergence of regional security interests after 9/11, which did create new institutional responses, under the current conditions, divergence in political interests and security inequalities have reduced the explanatory power of RSCT in North America. Relations between states in North American are becoming less characterized by the role of institutions and interests and more by identity politics in the region.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".