A Pragmatic Study of a Political Discourse from the Perspective of the Linguistic Adaptation Theory
Bibliographic record
Abstract
The emergence of the field of political marketing has highlighted the prominence of communication towards shaping the candidates’ image and building long-term relationships with voters. The linguistic characteristics of the political speech presented by candidates allow them to communicate to voters the superiority of his or her attributes over those of opponents (Kaid, 1999). Political campaigns are dynamic struggles between candidates to define the informational context for voters. Early researches (Kaid, 1981, 1986) suggested that political advertising has cognitive and behavioral effects on voters. It communicates the brand promise of a candidate blending functional and emotional benefits that voters gain from their relationships with a candidate. This study, based on Jef Verschueren’s (1999) Linguistic Adaptation Theory (LAT), proposes a pragmatic model for the analysis of a political election discourse. In this pragmatic model, it is shown that in such a discourse the process of adaptation to variables of the physical, social, and mental world is used. Such a process can be understood as the outcome of politicians’ choice making, dynamic negotiation and linguistic adaptation. The interpretation of a political discourse, on the other hand, can be better achieved by tracing the specific ways of meaning generation from the four focal points of context, structure, dynamics, and salience.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".