Bibliographic record
Abstract
Since political discourse portrays politicians’ knowledge state and their ideological assumptions, a critical analysis of Clinton’s speeches may unveil her perceptual and conceptual worlds. More specifically, CDA may uncover Clinton’s mental representations about the Tunisian Revolution and the US attitude towards such an important political event in North Africa and the Middle East. Studying factive presupposition and epistemic modality seems to be an effective pragmatic tool to reveal what is presented as factual or ideological knowledge in political discourse. The research instrument used to sort out the frequency distribution of lexical features, mainly factive and emotive verbs, factive noun phrases, mental state verbs and epistemic modal adjectives and adverbs, is the latest version of “AntConc” software. To uncover the epistemic state of Hillary Clinton, van Dijk’s (1995a) approach is implemented to analyze her speeches between January 2011 and December 2012. At the discourse level, research findings reveal that factive presupposition unveils the speaker’s strong personal commitment to the truth value of her propositions. At the cognitive level, results show that the speaker’s personal and social ideologies and knowledge are demystified by the cognitive mechanisms that govern discourse production and understanding via Idealized Cognitive Models (ICMs), cognitive frames and mental models. This study bridges the gap caused by the lack of research on factive vs. ideological knowledge in political discourse from a socio-cognitive perspective.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".