Emotional Practices and How We Can Trace Them: Diplomats, Emojis, and Multilateral Negotiations at the UNHRC
Bibliographic record
Abstract
ABSTRACT This article suggests a new approach for looking at emotions. In the framework that is developed, emotions are practices that are performed in context and not only felt or had. On the theoretical side, three concepts inspired by Bourdieu's work are introduced: hexis, emotional sense, and emotional performance. On the methodological side, this framework is used to make sense of emojis in digital exchanges. Emojis are the literal display of an emotion “on paper”—or rather, on screen—and constitute a simplified way to read the emotional communication between individuals. They are not epiphenomenal. Given the widespread use of instant messaging applications, they are an accessible and effective means for individuals to perform emotions. In turn, this framework opens up the possibility to analyze better how and why mundane emotions matter in international politics. How diplomats use emojis on WhatsApp during negotiations at the UN Human Rights Council in Geneva serves as an illustration. Often perceived as guided by rational calculations, diplomats also master informal and interpersonal skills to persuade, negotiate, and build connections. This fundamental social dimension of diplomatic work puts their (online) emotional practices at the center of their performances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".