Bibliographic record
Abstract
The aim of this paper is to develop the notions of particularism and generalism in argumentation theory. Generalism is the claim that to argue we need general rules that specify which data support which conclusions, while particularism denies it. The problem is that it is not always clear what these rules consist of, and in what sense argumentation depend on them. To clarify this, I will first introduce the discussion in moral philosophy and show how it has been adapted to argumentation theory. Then I will distinguish some ways of understanding rules and contend that their alleged necessity might be supported in at least three ways. This will allow me to identify some variants of generalism and, on this basis, to outline what I consider to be the most promising reading of particularism. L’objectif de cet article est de développer les notions de particularisme et de généralisme en théorie de l’argu-mentation. Le généralisme est l’affirma-tion selon laquelle pour argumenter, nous avons besoin de règles générales qui pré-cisent quelles données soutiennent que-lles conclusions, alors que le particula-risme le nie. Le problème est qu’il n’est pas toujours clair en quoi consistent ces règles et dans quel sens l’argumentation en dépend. Pour clarifier cela, je com-mencerai par introduire la discussion en philosophie morale et montrerai com-ment elle a été adaptée à la théorie de l’argumentation. Ensuite, je distinguerai quelques façons de comprendre les règles et soutiendrai que leur prétendue nécessité pourrait être soutenue d’au moins trois façons. Cela me permettra d’identifier certaines variantes du géné-ralisme et, sur cette base, d’esquisser ce que je considère comme la lecture la plus prometteuse du particularisme.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".