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Record W1621236257 · doi:10.1007/978-3-7908-1769-0_16

Two Values, Three Values, Many Values, No Values

2003· book-chapter· en· W1621236257 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.

Bibliographic record

VenueStudies in fuzziness and soft computing · 2003
Typebook-chapter
Languageen
FieldComputer Science
TopicLogic, Reasoning, and Knowledge
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsTruth valueProbabilistic logicSemantics (computer science)Extension (predicate logic)Formal semantics (linguistics)Computer scienceMathematicsArtificial intelligenceTheoretical computer scienceCalculus (dental)Algebra over a fieldProgramming languagePure mathematics

Abstract

fetched live from OpenAlex

Classical formal semantics is based on bivalence and truth-functionality. Historically, problems with these two notions have motivated the move from two values to three values and from three values to many values. We reflect on ordinary reasoning and systems of rational belief to motivate numerically based probabilistic semantics, which abandons truth-functionality. But numerically based probability theory is overly specific compared to real belief systems. So we develop a formal semantics based on comparative probability structures. Thus we develop a formal semantics which is not truth-functional and which does not use any values at all. The semantics is shown to be universal for any extension, with or without quantifiers, of classical sentence logic. We briefly discuss some areas for additional research.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.214
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.003
Research integrity0.0000.001
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 head0.045
GPT teacher head0.296
Teacher spread0.250 · 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