A Psycholinguistic Study of Political Rhetoric of Fear
Bibliographic record
Abstract
Political campaigns are dynamic struggles between candidates to define the informational context for voters. Early studies (Kaid, 1981, 1994a, 1994b) 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 Lakoff’s Framing Model (LFM, 2004), proposes a pragmatic model for the analysis of a political election rhetoric. Within this pragmatic model, it is shown that in such a rhetoric the process of choosing variables of mental and psychological strategies is used. Such a process can be understood as the outcome of producers’ choice making, dynamic negotiation and linguistic adaptation. The analysis of a political discourse makes it possible to see how frames are powerful rhetorical entities that motivate audience to filter their perceptions of the world. It presents evidences to the claim that a candidate’s speech using ‘rhetoric of fear’ appeals to the audience. Contradicted reactions appear: some audience react feeling ‘fearful’ while others respond feeling ‘protected’ or ‘heard’ that a candidate is listening to their concerns and willing to fulfil them. It also shows how the institutionalized use of strategy language has implications: some of these emerge from the genre itself while others derive from situation; specific choices.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".