Does changing the subject from A to B really provide an enlarged understanding of A?
Bibliographic record
Abstract
There are various ways of achieving an enlarged understanding of a concept of interest. One way is by giving its proper definition. Another is by giving something else a proper definition and then using it to model or formally represent the original concept. Between the two we find varying shades of grey. We might open up a concept by a direct lexical definition of the predicate that expresses it, or by a theory whose theorems define it implicitly. At the other end of the spectrum, the modelling-this-as-that option also admits of like variation, ranging from models rooted in formal representability theorems to models conceived of as having only heuristic value. There exist on both sides of this divide further differences still. In one of them, both the definiendum and definiens of a definition are words or phrases of some common natural language. In others, the item of interest is a natural language expression and its representation is furnished by the artificial linguistic system that models it. The modern history of these approaches is both very large and growing. Much of this evolution has given too short a shrift to the history of the demotion of ‘intuitive’ concepts in favour of the artificially contrived ones intended to model them. A working assumption of this article is that in the absence of a good understanding of what motivated the modelling-turn in the foundations of mathematics and the intuitive theory of truth, the whole notion of formal representability will have been inadequately understood. In the interests of space, I will concentrate on seminal issues in set theory as dealt with by Russell and Frege, and in the theory of truth in natural languages as dealt with by Tarski. The nub of the present focus is the representational role of model theory in the logics of formalized languages.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.008 | 0.031 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".