The Language of Diplomacy in Inaugural Speeches: Roberta Metsola’s Speech as the New President of the EU Parliament
Bibliographic record
Abstract
The study of inaugural speeches is fairly consolidated, especially if we refer to the analysis of the speeches of American presidents. Much less is available on the subject when we delve into lesser-known presidential figures, such as the presidents of European institutions. The analysis of the speeches of American presidents through the methodology of discourse analysis has helped to reveal their political intentions, their way of persuading listeners, building trust and empathy with the public, but also reaffirming their policies and measures relating to the economy, foreign affairs and social issues. In this paper I will focus on the new President of the European Parliament Roberta Metsola, elected on January 18, 2022. We are therefore in a completely different context, since the two forms of presidency are different in terms of roles, tasks and powers granted. Metsola’s inaugural speech will be analysed from a Discourse Analysis perspective, with a focus on lexical choice, personal pronouns and rhetorical figures to show how she plans to construct the image of the new presidency of the EU parliament and her intended objectives. The paper analyses how those who work in institutions such as the EU try to convey political messages, maintain or change the point of view of their public on certain issues, and understand whether they tend to conform to the consolidated structural and linguistic protocols that characterize their role or whether they (may) lapse into discursive practices that sometimes seem trite and worn-out.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".