Quotations and Presumptions: Dialogical Effects of Misquotations
Bibliographic record
Abstract
Manipulation of quotation, shown to be a common tactic of argumentation in this paper, is associated with fallacies like wrenching from context, hasty generalization, equivocation, accent, the straw man fallacy, and ad hominem arguments. Several examples are presented from everyday speech, legislative debates and trials. Analysis using dialog models explains the critical defects of argumentation illustrated in each of the examples. In the formal dialog system CB, a proponent and respondent take turns in making moves in an orderly goal-directed sequence of argumentation in which the proponent tries to persuade the respondent to become committed to a conclusion by asking questions and offering arguments. Analyzing quotation by using the notion of commitment in dialog, it is shown (a) how an arguer’s previous assertions can be brought to light in the course of a dialog to deal with problems arising from misquotation, (b) how the profile of dialog model allows a critic to analyse the fundamental effects misquotation brings about in a dialog, and (c) how the critic can use such an analysis to correct the problem.
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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 | 0.011 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".