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Record W2922258449 · doi:10.3917/rip.286.0393

Practical reasoning and the act of naming reality

2018· article· en· W2922258449 on OpenAlexaff
Fabrizio Macagno, Douglas Walton

Bibliographic record

VenueRevue internationale de philosophie · 2018
Typearticle
Languageen
FieldComputer Science
TopicMulti-Agent Systems and Negotiation
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsDeliberationArgumentation theoryFraming (construction)EpistemologyComputer scienceAction (physics)Practical reasonValue (mathematics)Process (computing)Ethical dilemmaDilemmaManagement scienceArtificial intelligencePolitical sciencePhilosophyLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.812
Threshold uncertainty score0.181

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.071
GPT teacher head0.331
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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