Quotations and Presumptions: Dialogical Effects of Misquotations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it