Bibliographic record
Abstract
Abstract Argumentation on some public policy issues is conjugated with disagreement and power differentials. Institutionally dominant arguers control the argumentation context through imposing authority rules which sometimes incentivize them to respond to opposing arguers in a fallacious way 1 – with “the repeating tokens of the same counterarguments” and without considering the merits of opposing arguments. As produced in accordance with authority rules, such fallacies are embedded in the dominant argumentative discourse and easily pass unnoticed. To detect them, I introduce Argument Continuity (AC) – a new category of argumentative discourse analysis. AC is a set of the same arguments and counterarguments repeatedly produced/reproduced by the dominant arguer through an adversarial reasoning process to disconfirm opposing arguments and dismiss them. ACs are distinguished from other fallacies by their continuous nature and recursive way of production. ACs have their own life cycle – a chain of reasoning dynamics developing in a path-dependent fashion and increasing the cost of adopting a certain argument over time. I test the life cycle of ACs in a single case study – in consultations held by the Crown with Indigenous peoples of Canada over a controversial resource development project. Although ACs are not specific to the Crown-Indigenous relationships, they reveal how dominant arguers treat disagreement from epistemically diverse arguers. Based on observed evidence, I develop three theoretical propositions of ACs, which can serve as guidelines for researching the disconfirming mode of reasoning in other contexts of communication permeated by beliefs clash and power asymmetries.
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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 | 0.026 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.006 | 0.067 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".