Bibliographic record
Abstract
In this paper, I argue that the Neg particles head their projections, and the negation in a hierarchical representation occurs between TP and VP. In future tense, I argue that the Aux can move to the Neg head just to pick the negation and then the negative particle and the Aux moves to T. I also show that speakers of RJA use different negation constructions depending on the structure and tense of the sentence. For example, the negative particle ma is a preverbal particle used with present and past verbs evenly. The negative particle ma¦-ƒ is a pre and post-verbal particle where ma is a proclitic and -ƒ is an enclitic. This particle is used with present verbs and past verbs. However, when used with present tense verbs, the proclitic ma becomes optional, whereas with past tense verbs the deletion of the proclitic ma results in an ungrammatical sentence. As for copular sentences, the particle miƒ is used to negate verbless copular sentences where there is a covert present tense verb. But, when the copular sentence is formed via a past tense verb, miƒ is no longer used. Instead, the negative construction maâ¦-ƒ is used.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".