The correlation between negative strategies and basic word order
Bibliographic record
Abstract
Based on two typological frameworks (Dahl, 1979 and Miestamo, 2007), I explore the various strategies used to negate declarative verbal main clauses (standard negation) in 28 languages in order to investigate the correlation between them and basic word order. The 28 languages are divided into three groups according to their basic word order as follows: 11 SOV, 10 SVO and 7 VSO. As much as possible, I have included languages from different language families and different geographical areas in order to eliminate the effect of genetic relationships and borrowings. The results suggest that negative strategies are probably morphological, where the negator is an affix, in SOV languages and frequently syntactic, where the negator is an independent morpheme, in SVO and VSO languages. I also show that symmetric negation, where no structural differences are observed between affirmatives and negatives other than the negative marker (s), is the most common type cross-linguistically.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".