Bibliographic record
Abstract
Abstract This study argues that many of the formalizations used in analyses employing the notion of logical scope fail to conform to natural language in important ways and lead to false predictions. This is due to the fact that they pursue the logic-driven goal of making the structure of logical arguments more transparent and mechanically calculable rather than the language-driven goal of accounting for how the linguistic signs used in an utterance and their configuration contribute to the conveying of the message being fashioned by the speaker. The focus of the study is on categories associated with the verb: tense, aspect, modality and negation. The conclusion suggests that very precise and rigid theories using logical scope relations may force the theorist to straitjacket the data so that they fit the theory, thereby obscuring rather than clarifying the nature of linguistic categories and their interactions. Informal analyses that hew closer to natural language’s semantic reality can provide greater understanding of phenomena such as the purported non-negatability of must. Seeing this English modal’s meaning as defined in opposition to real existence leads to the realization that it does not interact with negation the same way as the reality of the existence of the property of being necessary does.
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 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 | 0.022 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.071 |
| Scholarly communication | 0.009 | 0.029 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".