Guías de buenas prácticas y prácticas discursivas "no tan buenas": los topoi sedimentados en el discurso institucional
Bibliographic record
Abstract
El objetivo de este trabajo es ver cómo, en aras de realizar actividades de imagen (de afiliación y autonomía), con efectos socio-comunicativos beneficiosos para el emisor (locutor) del discurso, se terminan asentando y sedimentando topoi asociados a malas prácticas sociales que, lejos de ser relativizados y/o destruidos, quedan, en cierto modo, reforzados y mantenidos en el seno social. Esto se traduce en la posibilidad de ser empleados nuevamente en una argumentación contraria a los objetivos institucionales previstos. \nPara llevar a cabo este estudio desde una perspectiva en la que se combinan presupuestos pragmalingüísticos, como los relacionados con la teoría de la argumentación, y la pragmática sociocultural, hemos utilizado manuales y guías de buenas prácticas de diversas instituciones nacionales, autonómicas y de organizaciones no gubernamentales (ONG).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".