Quantification And Predication In Modal Predicative Propositional Logic
Bibliographic record
Abstract
The main objective of this paper is to enrich first order propositional predicative logic by dealing all together with intensional attributes, quantification, logical and historic modalities and ramified time. Predicative propositional logic advocates a finer analysis in terms of predication of the logical form of propositions. Like Church’s logic of sense and denotation (Church 1951), my new predicative approach of quantification is based on Frege’s theory of indirect reference (Frege 1892). However in my approach, like in algebraic intensional logic, generalized propositions predicate first order generalizations of attributes. In the first section, I will analyze in terms of predication the logical form of elementary propositions with all kinds of attributes (whether intensional or extensional) and of generalized, modal and temporal propositions. Next I will define the ideographic object-language of my logic. Its formulas can express different propositions in different contexts of utterance. In the third section, I will define the structure of a standard model for my ideography. In the last section, I will enumerate new valid laws of my logic.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".