Habermas, Argumentation Theory, and Science Studies: Toward Interdisciplinary Cooperation
Bibliographic record
Abstract
This article examines two approaches to the analysis and critical assessment of scientific argumentation. The first approach employs the discourse theory that Jurgen Habermas has developed on the basis of his theory of communicative action and applied to the areas of politics and law. Using his analysis of law and democracy in his Between Facts and Norms (1996) as a kind of template, I sketch the main steps in a Habermasian discourse theory of science. Difficulties in his approach motivate my proposal of an alternative approach that starts not with a theory of communicative action but with some broad categories drawn from argumentation theory. Using these categories, one can survey the various conceptions of scientific argumentation that have already emerged in the multi-disciplinary field of science studies. The more flexible, open-ended theoretic categories put one in a better position to'develop cooperative interdisciplinary studies that can inform the critical assessment of scientific argumentation.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".