Bibliographic record
Abstract
The book defines the concept of Semantic-Communicative Structure [= Sem-CommS]-a formal object that is imposed on the starting Semantic Structure [= SemS] of a sentence (under text synthesis) in order to turn the selected meaning into a linguistic message. The Sem-CommS is a system of eight logically independent oppositions: 1. Thematicity (Rheme vs. Theme), 2. Givenness (Given vs. Old), 3. Focalization (Focalized vs. Non-Focalized), 4. Perspective (Foregrounded vs. Backgrounded), 5. Emphasis (Emphasized vs. Non-Emphasized), 6. Presupposedness (Presupposed vs. Non-Presupposed), 7. Unitariness (Unitary vs. Articulated), 8. Locutionality (Communicated vs. Signaled). The values of these oppositions mark particular subnetworks of the starting SemS and thus allow for the distinction between sentences such as (a) A man killed a dog vs. The dog was killed by a man, (b) John washed the window vs. It was John who washed the window or (c) It hurts! vs. Ouch! The proposed Sem-Comm-oppositions are conceived as an attempt at sharpening the well-known notions of Topic ~ Comment, Focus, etc. Possible linguistic strategies for expressing the values of the Sem-Comm-oppositions in different languages are discussed at some length, with linguistic illustrations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".