Bibliographic record
Abstract
Abstract Health professionals and the public puzzle through new or controversial issues by deploying patterns of reasoning that are found in a variety of social contexts. While particular issues and vocabulary may require field specific training, the patterns of reasoning used by health advocates and authors reflect rhetorical forms found in society at large. The choices made by speakers often impact the types of evidence used in constructing an argument. For scholars interested in issues of policy, attending to the construction of arguments and the dominant cultural modes of reasoning can help expand the understanding of a persuasive argument in a health context. Argumentation scholars have been attentive to the patterns of reasoning for centuries. Deductive and inductive reasoning have been the most widely studied patterns in the disciplines of communication, philosophy, and psychology. The choice of reasoning, from generalization to specific case or from specific case to generalization, is often portrayed as an exclusive one. The classical pattern of deductive reasoning is the syllogism. Since its introduction to the field of communication in 1957, the Toulmin model has been the most impactful device used by critics to map inductive reasoning. Both deductive and inductive modes of argumentative reasoning draw upon implicit, explicit, and affective reasoning. While the traditional study of reasoning focused on the individual choice of a pattern of reasoning to represent a claim, in the last 40 years, there has been increasing attention to social deliberative reasoning in the field of communication. The study of social (public) deliberative reasoning allows argument scholars to trace patterns of argument that explain policy decisions that can, in some cases, exclude some rhetorical voices in public controversies, including matters of health and welfare.
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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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".