Bibliographic record
Abstract
Within the political sphere, a political actor is often judged by what he or she says, with their verbal performance often perceived as representative of the individual. Hearers accept that, as individuals, they possess a lifetime of experiences and actions which inform, but may also undermine, their aspirations in gaining political capital. Additionally, as representatives of a political party and its ideology, these actors do not exist in isolation; they are members and, at times, potential candidates of a particular party with its own agenda which may, in turn, cause them to modify their personal speech to align with espoused policies of the party. The various contributions contained in this volume examine the discourse of political actors through the lenses of positionality and stance. Throughout its chapters, clearly defined theoretical perspectives and specified social practices are employed, enabling the authors to elucidate how political actors can situate themselves, their party, and their opponents toward their ostensive public. This book successfully demonstrates how espoused perspectives relate to, or reflect on, the nature of the individual political actor and their truth, the party they represent and its ideology, and the pandering to popular public opinion to gain support and co-operation. This book will hold particular appeal for postgraduate students, researchers, and scholars of discourse studies, pragmatics, political science, as well as other areas in humanities and the social sciences.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.044 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".