The Logical Pragmatics of Arguments in Argumentation
Bibliographic record
Abstract
Logic is understood so far as a product perspective, either formal or informal. The topic is still, though interesting, imprecise, sketchy and problematic. Besides, the relevance of logic to linguistics has not been explained. This research focuses on dealing with logic as a product and a process. It introduces how logic is relevant to understanding language. Logic is surely not irrelevant to real human language. In this research, we coin 'logical pragmatics' to refer to "the structure of an argumentation and its parts used by the speaker for the purpose of persuasion to have an effect in the addressee and passive audience”. As such, the research mainly aims at providing a definition of "logical pragmatics" as well as developing an ideal model for it. To accomplish this aim, the research studies what this approach entails and the relevance of logic and pragmatically oriented contributions to the field of argument and argumentation. The study mainly concluded that in real communication, simple logical relations become very complex and part of a wider context where we have a speaker's communicative intention, a hearer's communicative inference, and context.
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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 | 0.029 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.053 |
| Scholarly communication | 0.021 | 0.036 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".