The changing practices of frontline diplomacy: New directions for inquiry
Bibliographic record
Abstract
Abstract This article develops the concept of ‘frontline diplomacy’ – what practitioners referring to work in embassies, consulates, and permanent representation as ‘the field’ –, defined here as all diplomats’ activities taking place away from headquarters. IR scholarship tends to focus on Ministries of Foreign Affairs located in capitals. On the contrary, building on the practice turn in IR, we first show that international politics emerge from frontline practices. Adding to criticism against the practice turn, we then explain that it has missed important transformations occurring in frontline diplomacy because it tends to privilege stability over change. We finally discuss two innovations in frontline practices: the action of Sherpas in G20 summits following the 2008 crisis and the use of Twitter by US Ambassador to Russia Michael McFaul (2012–14). For each we answer three questions: How do these activities transform traditional modes of operation? How are non-state actors involved in them? What do they tell about transformation of global politics? Because diplomatic practices at the frontlines epitomise international politics, these new directions for inquiry contribute substantively to IR scholarship. At the theoretical level, they enrich the continuing encounter between IR and diplomatic studies through practice theory and help to understand change in practice.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.013 | 0.035 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".