The Diplomacy of Proximity and Specialness: Enhancing Canada's Representation in the United States
Bibliographic record
Abstract
Abstract Diplomatic representation, both as a concept and in terms of its structures and processes, does not receive the attention that it deserves. This is surprising given that it forms a central concern for both analysts and practitioners of diplomacy, with the latter confronting multiple challenges in adapting modes of representation to changes in their international and domestic political environments. One facet of this can be identified in responses to factors that have assumed a significant place in the development of diplomacy — namely distance and proximity. To the growth of proximity in both spatial and issue-oriented terms, the challenge of the 'special relationship' is added in specific contexts. Both factors come together in the case of Canada's attempts to manage its policies towards the United States. Here, strategies have moved through distinct phases responding to domestic and international changes. The latest phase, which is associated with substantial rethinking of the role and structure of Foreign Affairs Canada, assumes the form of what has been labelled the Enhanced Representation Initiative (ERI). The ERI is interesting not only in the Canadian-US context, but because it reveals more general problems for governments seeking to manage the pressures of proximity and a growing number of relationships that assume aspects of 'specialness'.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.007 |
| 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".