Bibliographic record
Abstract
Rather than the art of putting forward logically valid arguments leading to Truth, argumentation is here viewed as the use of verbal means ensuring an agreement on what can be considered reasonable by a given group, on a more or less controversial matter. What is acceptable and plausible is always coconstructed by subjects engaging in verbal interaction. It is the dynamism of this exchange, realized not only in natural language, but also in a specific cultural framework, that has to be accounted for. From this perspective, it is not enough to reconstruct patterns of reasoning. As logos is by definition both Reason and Language, abstract schemata have to be examined in their verbal realization in a given situation of discourse. Such an approach toarguments allows for a “thick” description taking into account their discursive and communicational aspects, as well as argumentation’s constitutive dialogism and its inscription in a set of common representations, opinions and beliefs (a doxa).This approach, exemplified by the analysis of a short text on stock options borrowed from the French newspaper Libération, is an attempt at establishing a dialogue between disciplines like argumentation theories, rhetorical criticism and discourse analysis.
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 | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.009 | 0.060 |
| Scholarly communication | 0.027 | 0.019 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 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".