Bibliographic record
Abstract
Infiltration of a word’s meaning by world-knowledge is argued to be consistent with the semiological principle. While acknowledging variability in what people know about elephants, there is a common core of what everybody knows that we know we can evoke in anybody’s mind; this constitutes the meaning of the word “elephant”. Regarding truth-conditional semantics, to say that the difference between “dog” and canis familiaris “is not a semantic difference; it is not a difference in what they mean” is to equate meaning with truth-value. This would entail that the complex NP direct object in “I took the four-legged fur-bearing carnivorous animal that barks out for a walk” would have the same meaning as the noun “dog”. From a linguistic point of view, this is completely indefensible. My criticism that the truth-conditional approach erroneously takes sentences to be the basic sign/meaning unit is not obviated by the fact that truth-conditional semantics treats sentence meaning as compositional, the point being that sentences are clearly not pairings of sounds with meanings since they do not have stable meanings which could be paired off with their linguistic forms. This is argued to be the case even if one defines meaning as Logical Form.
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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.028 | 0.031 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".