Diálogos de indagación como estrategia para mitigar los riesgos deliberativos del discurso populista
Bibliographic record
Abstract
Populist discourse constitutes “the people” discursively and declares its interests, goals or values, such as the struggle against the elite. In order to satisfy the “people”, these interests, goals or values must be realized. However, these interests do not necessarily correspond to what the majority of citizens would prefer. Thus, if, in the absence of reliable information about what the members of a society want and prefer, each citizen deliberates about what should be done, taking as a starting point only the populist discourse, citizen deliberation may be ill-founded, it may assume that most people are interested in what populist discourse says is in the interest of “the people”. To prevent and remedy these deliberative risks, we propose to make large-scale use of the type of dialogues that, following Walton and Krabbe, we call “inquiry dialogues”.
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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 | 0.024 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| 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".