Rhetorica: The means to success in digital diplomacy?
Bibliographic record
Abstract
With the advent of the Internet, the ways in which politicians address their publics have changed.However, their willingness to persuade their people, or 'followers', has not.This thesis' research hopes to get behind the 'veil' of what impacts politicians' power and popularity, of what provides them with 'soft power', specifically on social networking sites like Twitter.It tries to do so by using a rhetorical framework, as seen in NGO-and PR research, based on Aristotle's Rhetorica.Did the famous Greek philosopher not already say in 330 BC "Rhetoric is the faculty of discovering in the particular case what are the available means of persuasion"?From robust statistical analyses of the tweets of the Prime Minister of Canada Justin Trudeau (an example of a new generation of technology-prone leaders), it becomes clear that especially the rhetorical tool of Pathos, the use of emotions and motivational messages, is important in online political communication.Ethos and Logos, its rhetorical counterparts, seem to be less impactful for politicians' digital (Twitter) diplomacy.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".