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Record W4406156265 · doi:10.22329/il.v44i4.8517

How do Explanations Justify?

2025· article· fr· W4406156265 on OpenAlexfundvenueno aff
Petar Bodlović, Marcin Lewiński

Bibliographic record

VenueInformal Logic · 2025
Typearticle
Languagefr
FieldComputer Science
TopicSemantic Web and Ontologies
Canadian institutionsnot available
FundersCHIST-ERAUniversité de FribourgEuropean CommissionFundação para a Ciência e a TecnologiaUniversity of Windsor
KeywordsEpistemologySociologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract: The paper presents an extended scheme for the inference to the best explanation (IBE). The scheme precisely treats the epistemic modifiers (“hypothetically,” “plausibly,” “presumably”) of the inference, acknowledges its contrastive nature, clarifies the logical support between premises and conclusions (linked, convergent, and serial support), and introduces additional premises essential for inferring justified conclusions (especially those related to causal explanations and more demanding standards of proof). Overall, it advances the existing schemes for IBE in argumentation theory and treats IBE as a par excellence argumentative, rather than explanatory, form of reasoning. Résumé: L’article présente en détail un schéma pour l’inférence vers la meilleure explication (IME). Le schéma traite précisément les modificateurs épistémiques (« hypothétiquement », « plausiblement », « vraisemblablement ») de l’inférence, reconnaît sa nature qui fait contraste, clarifie l’appui logique entre les prémisses et les conclusions (l’appui lié, convergent et sériel) et introduit des prémisses supplémentaires essentielles pour inférer des conclusions justifiées (en particulier celles liées aux explications causales et aux normes de preuve plus exigeantes). Dans l’ensemble, l’article fait progresser les schémas existants pour l’IME dans la théorie de l’argumentation et traite l’IME comme une forme de raisonnement argumentatif par excellence, plutôt qu’explicatif.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.914
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
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 head0.048
GPT teacher head0.284
Teacher spread0.236 · 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 designNot applicable
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

Citations0
Published2025
Admission routes2
Has abstractyes

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