Verum focus is verum, not focus: Cross-linguistic evidence
Bibliographic record
Abstract
The accent pattern known as verum focus is commonly understood as an ordinary alternative focus on the truth of a proposition. This standard view, which we call the focus accent thesis (FAT), can be contrasted with the lexical operator thesis (LOT), according to which the accent pattern that looks like focus in languages like German or English is actually not an instance of focus marking, but realizes a lexical verum predicate, whose function is to relate the current proposition to a question under discussion. Although it is hard to distinguish between the FAT and the LOT on the basis of German or English, a broader cross-linguistic perspective seems to favor the LOT. Drawing from fieldwork on Tsimshianic (Gitksan) and Chadic (Bura, South Marghi), we first show that in none of these languages is verum realized in the same way that ordinary alternative focus is marked. This sheds initial doubt on the unity of verum and focus. Secondly, the FAT predicts that a language cannot have co-occuring verum and focus, if it does not allow multiple foci, and that a language should allow them to co-occur if it allows for multiple foci. Again, while it is hard to find counterexamples in German or English, some of the data from our cross-linguistic investigation favor the LOT.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".