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Record W2792688094 · doi:10.1080/0020174x.2017.1402698

Assessment context-sensitive logical claims

2018· article· en· W2792688094 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInquiry · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicEpistemology, Ethics, and Metaphysics
Canadian institutionsUniversity of Windsor
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsContext (archaeology)EpistemologyLogical consequenceLogical truthTasteRelation (database)Coherence theory of truthLogical conjunctionLogical formPhilosophyComputer sciencePsychologyPragmatic theory of truthLinguisticsHistory

Abstract

fetched live from OpenAlex

Several philosophers have recently developed accounts of relative (or assessment context-sensitive) truth. Given that logical consequence is often characterized in terms of truth preservation, notions of truth are often associated with corresponding notions of logical consequence. Accordingly, in his Assessment Sensitivity: Relative Truth and Its Applications, John MacFarlane provides two different definitions of logical consequence that incorapte assessment context-sensitive truth. One motivation for adopting an assessment context-sensitive account of truth for judgements about taste is to explain how conflicting taste claims can be true relative to different contexts of assessment. However, in the midst of dialogues in which conflicting taste claims are made, it is also possible for the participants in the dialogue to make conflicting claims about what inferences are and are not logically valid. This paper accomplishes two objectives. First, I argue that MacFarlane’s notions of logical consequence do not adequately account for important features of some dialogues in which conflicting logical claims are made. In particular, I argue that MacFarlane’s accounts of logical consequence do not explain how logical claims made about inferences in taste-discourse could be assessment context-sensitive. Second, I propose a consequence relation that can be incorporated into an assessment context-sensitive account of logical claims made about inferences in taste discourse.

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.

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 categoriesInsufficient payload (model declined to judge)
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 score0.893
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.178
GPT teacher head0.358
Teacher spread0.180 · 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