Bibliographic record
Abstract
In the tradition stemming from Aristotle through Aquinas, rational decision making is seen as a complex structure of distinct phases in which reasoning and will are interconnected. Intention, deliberation, and decision are regarded as the fundamental steps of the decision-making process, in which an end is chosen, the means are specified, and a decision to act is made. Based on this Aristotelian theoretical background, we show how the decision-making process can be modeled as a net of several patterns of reasoning, involving the classification of an action or state of affairs, its evaluation, the deliberation about the means to carry it out, and the decision. It is shown how argumentation theory can contribute to our understanding of the mechanisms involved by formalizing the steps of reasoning using argumentation schemes, and setting out the value-based criteria underlying the evaluation of an action. Representing each phase of the decision-making process as a separate scheme allows one to identify implicit premises and bring the roots of ethical dilemma to light along with the means to resolve them. In particular, we will show the role of framing and classification in triggering value-based reasoning, and how argumentation theory can be used to represent and uproot the grounds of possible manipulations.
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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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.079 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".