Studying Argumentation Behaviour / Estudiando el comportamiento argumentativo
Bibliographic record
Abstract
Abstract: Starting from the observation that argumentation studies have low recognition value both within and without the academy, and mindful of the current desiderata that academic research should be relevant outside the academy, I introduce the concept of an argumentation profile as a panacea for our ills. Argumentation profiles are sketches of the argumentation behaviour of either individuals or groups (such as political parties) and are based on concepts unique to argumentation studies such as argumentation schemes, dialogical roles and responsiveness. It is argued that argumentation profiles would be of interest to voters as well as political parties. Resumen: Comenzando por la observacion de que los estudios de la argumentacion tienen un bajo valor de reconocimiento dentro y fuera de la academia, y consciente del actual desideratum de que la investigacion academica deberia ser relevante fuera de la academia, introduzco el concepto de un perfil argumentativo como un remedio a nuestros problemas. Los perfiles argumentativos son borradores del comportamiento argumentativo de sus agentes y grupos (como los partidos politicos) y estan basados en conceptos particulares de los estudios argumentativos tales como esquemas argumentativos, roles dialogicos y sensibilidad argumentativa. Se sostiene que los perfiles argumentativos deberian ser de interes para los votantes como para los partidos politicos.
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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".