Bibliographic record
Abstract
We argue that common knowledge, of the kind used in reasoning in law and computing is best analyzed using a dialogue model of argumentation (Walton & Krabbe 1995). In this model, implicit premises resting on common knowledge are analyzed as endoxa or widely accepted opinions and generalizations (Tardini 2005). We argue that, in this sense, common knowledge is not really knowledge of the kind represent by belief and/or knowledge of the epistemic kind studied in current epistemology. This paper takes a different approach, defining it in relation to a common commitment store of two participants in a rule-governed dialogue in which two parties engage in rational argumentation (Jackson & Jacobs 1980; van Eemeren & Grootendorst 2004). A theme of the paper is how arguments containing common knowledge premises can be studied with the help of argumentation schemes for arguments from generally accepted opinion and expert opinion. It is argued that common knowledge is a species of provi- sional acceptance of a premise that is not in dispute at a given point in a dia- logue, but may later be defeated as the discussion proceeds
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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.012 | 0.032 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".