Bibliographic record
Abstract
Our intuition is straightforward: yes, argumentation changes minds. It can’t be otherwise! But many cognitive and discursive habits seem to suggest otherwise. As the literature in the psychology of reasoning incessantly emphasizes, we hardly change our minds (and the minds of others) because a predisposed robust confirmation bias (or myside bias) is at work every time we argue, among other persistent cognitive illusions (Pohl, 2012), heuristics and biases (Santibáñez, 2023). To adequately answer the questions of why and how argumentation changes minds, if at all, this paper frames the problem in an evolutionary perspective. My main thesis is that argumentative competence changes minds because its ultimate goal (Laland et al., 2011; Scott-Phillips, Dickins & West, 2011) is to construct the future to predict more accurately (Suddendorf, Redshaw & Bulley, 2022). This idea converges with some evolutionary analysis of other cognitive skills and cultural inventions. To explain my perspective, I use the distinction between ultimate and proximal goals of a trait, and the cultural background of argumentative competence plays a fundamental role within this distinction. Notre intuition est simple: oui, l’argumentation change les men-talités. Mais de nombreuses habitudes cognitives et discursives suggèrent le contraire. Comme le soulignent sans cesse les écrits sur la psychologie du raisonnement, nous ne changeons guère d’avis parce qu’un biais de con-firmation robuste (ou biais de-mon-côté) prédisposé est à l’oeuvre lorsque nous argumentons. Pour répondre adéquatement aux questions de pour-quoi, comment et si l’argumentation change les mentalités, je pose le prob-lème dans une perspective évolution-niste. Je soutiens que la compétence ar-gumentative change les mentalités parce que son but ultime est de con-struire l’avenir, de prédire avec plus de précision. Cela converge avec les anal-yses évolutionnistes d’autres compé-tences cognitives et inventions cul-turelles. Pour expliquer ma perspec-tive, j’utilise la distinction entre les buts ultimes et proximaux d’un trait.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".