Commonwealth Diplomatic Missions a Comparative Empirical Investigation of the Foreign Policy of Five Commonwealth Members
Bibliographic record
Abstract
How do Commonwealth members decide to allocate resources to send diplomatic representation to other countries? For a cross-section of 150 countries, the author performs a logistical regression for five Commonwealth countries (Australia, Bangladesh, Canada, Cyprus, Kenya) to determine if Commonwealth membership plays a rôle in the placement of embassies and high commissions abroad. The model takes power, regionalism, and Commonwealth membership into account as explanatory variables to explain the allocation of embassies and high commissions abroad. The findings show the importance of power and geography in allocating resources, suggesting the value of the realist and geopolitical explanations in analysing foreign policy. The only country in the sample that shows any evidence of Commonwealth membership playing a rôle in the allocation of embassies and high commissions is Kenya. other countries to achieve political objectives and to support economic growth. In the following section I will explore arguments for how a Commonwealth state should allocate scarce resources in the conduct of its foreign policy. Then I will test these propositions to see if Commonwealth countries generally allocate resources consistently with the arguments. The conclusion will illustrate what has been learned about Commonwealth members' foreign policy and which questions are raised for future research.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".