Consular diplomacy's first challenge: Communicating assistance to nationals abroad
Bibliographic record
Abstract
Abstract This article discusses the first hurdle for consular diplomacy in the digital age: the communicative challenge. Providing information and assistance to nationals abroad is a major challenge, and governments are well advised to go about this activity in a more citizen‐centric fashion. It is therefore important for ministries of foreign affairs (MFAs) and their consular divisions to acquire a deeper understanding of their nationals' communicative behaviour. Creativity from a new generation of tech‐savvy diplomats is going a long way in applying digital tools to consular challenges, but greater control across communication channels, and therefore management capacity, is required. Getting through to citizens in a fragmented communication environment in the 2020s implies the strategic coordination of various forms of offline and online communication. Framing consular services in market terms and identifying citizens as customers would, however, go against the MFAs' own interests. Governments would do well to view consular assistance as part of their growing diplomatic engagement with domestic society. Analysis of consular policy and practice also suggests that there are good reasons for MFAs to articulate existing links between consular assistance and wider foreign and security policy, rather than seeing ‘consular’ as a self‐contained activity.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".