Bibliographic record
Abstract
Abstract This paper approaches symbolic insult in diplomacy as the use of symbolic means by states to oppress the opponent’s sense of Self, to hurt its self-esteem and social status in order to achieve their foreign policy objectives, or as a reaction to a threat from Other. The paper posits that diplomatic actors are extremely sensitive to Self related matters, and may use such sensitivity for influencing each other, bargaining over the issues of importance, and simply defending their sense of Self while they confront the opponent. The enormous importance of collective Self in diplomacy may instigate a variety of social strategic games, including tacit and deceptive ones. The diplomatic actor’s acute sensitivity to recognition, honor and social status sharpens its sense of Self, which makes any humiliation painful. Therefore, protection of self-regard, dignity and public face becomes a critical issue in diplomatic practice. At the same time, that makes the diplomatic actor’s Self vulnerable, and provides the opposing Other opportunities for manipulation and symbolic abuse. The paper argues that symbolic insult in diplomacy occurs in a highly normative environment, and depends on political objectives, shared knowledge, social perception and practices, and can negatively affect relationships between diplomatic actors, the opponent’s self-perception, self-feeling and security of Self—ontological security. I distinguish three forms of symbolic insult used in diplomacy: by misrecognition (“diplomatic bypassing”), direct confrontation (“diplomatic punch”) and concealed verbal or nonverbal actions (“diplomatic slap”). The paper focuses on the third, indirect form, or “diplomatic slap” which employs obscure symbolic insults as a means of tacit manipulation for influencing the opponent, or as an instrument of restoring social status. By highlighting interest-based (political), value-based (moral), relationship-based (social) and right-based (legal) imperatives of international diplomacy, this paper shows that diplomatic actors can use symbolically expressed but subtle “slap” for balancing their interests and relationships in dealing with the opponent: Tacit or implicit symbolic insult usually appears ambiguous which may allow the offender to promote its interests but also to stay engaged with the victim.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".