Bibliographic record
Abstract
The paper opens by defining 'logical universality' as the retention of the propositional content of expressions under any enunciative circumstances. Universality in this sense, the paper claims, cannot be demonstrated in the same manner across different discursive domains and sign systems. Unlike in geometry, arithmetic, algebraic and mathematical logic, where logical universality can be shown to be non-controversial, the concept of universality becomes problematic as soon as natural language terms and syntax are employed. The paper shows the main reasons for this difficulty to lie in the extensional features of natural language, which cannot be adequately captured by intentional means. Intentional descriptions are claimed to apply only to semiotically homogeneous sign systems of a formal kind. Natural language expressions, in contrast, are semiotically heterogeneous, or heterosemiotic, characterised as they are by quasi-perceptual ingredients. Nevertheless, the paper argues, there are three cases in which logical universality can be demonstrated to hold in spite of natural language being employed, one of which is strictly technical language. In contrast, culturally fully saturated natural language use is shown to escape the constraints of logical universality as defined, on the grounds that some of its essential features, such as referential background, reference, and deixis, especially in its implicit form, effectively undermine the retention of identical propositional contents across cultures and time.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".