Understanding fallacious reasoning <i>via</i> detecting moves of motivated criticism in Argument Continuity—a new strategy of circular reasoning in coping responses to disagreement within a distorted reasoning context
Bibliographic record
Abstract
Reasoning asymmetry arises when biased argument production aligns with biased argument evaluation by the same arguer, a phenomenon commonly termed motivated criticism or biased assimilation. This practice, often wielded by individuals in positions of power, aims to advocate for specific decision options. Argument Continuity exemplifies this asymmetry within a distorted reasoning context, where a motivated critic incessantly reiterates arguments in counterarguments to discredit less powerful opponents, disregarding evidential priority in reasoning exchanges. When these restatements are bolstered by appeals to authority, uncertainty, or the unlikelihood of adverse effects of a decision option, they signify moves of motivated criticism, perpetuating Argument Continuity discursively. The paper seeks to identify and annotate instances of Argument Continuities in Indigenous consultation reports, reconstructing a discourse of motivated criticism among officials responding to resource concerns. By developing annotation guidelines, it aims to classify and predict Argument Continuity, providing a tool to preempt fallacious reasoning by authorities across diverse public policy contexts.
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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 | 0.016 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".