Variation in the pronunciation/silence of the prepositions in locative determiners
Bibliographic record
Abstract
We argue that the micro-variation observed in the pronunciation/silence of the prepositional head of locative determiners in Fallese, a dialect spoken in Abruzzi, follows from the option of valuing features by either External Merge or by Internal Merge, given Spell-Out conditions, whereas this option is not available in English and Italian. It follows that the prepositional head is silent in Italian and English, whereas it can be pronounced in Fallese when the Specifier of the locative determiner is not filled. We show that this feature-based approach to micro-variation, in conjunction with principles of efficient computation, makes correct predictions for the pronunciation of the prepositional head in other functional categories, as well as it makes predictions on the diachronic development of locative determiners Latin to Fallese and from Latin to Italian’, otherwise it looks like Fallese is an old stage of Italian.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".