Bibliographic record
Abstract
This paper discusses morphological alternations in the so-called strong preterits in Spanish. The starting point is the variation phenomenon known as Analogical Strong Preterits, which is characterized by the formation of 3PL on the basis of 3SG, adding the exponent /n/: dij-o-n, instead of general dije-ro-n ‘they said’. Although this is the way in which the 3PL is obtained in the rest of the paradigm, Spanish preterits regularly present the segment -ro- at the left of -n. The varieties with Analogical Strong Preterits preserve the segment -ro- in all the other verbs (canta-ro-n ‘they sang’), but shows this particular form in the case of strong preterits. Within the framework of Distributed Morphology, we explore the morphological alternations found in relation to verbal stems (dec-/dij- for √SAY) and functional morphology (-ro/-o for T/Pers). Our approach focuses on the properties of Vocabulary Items and the locality of terminal nodes for vocabulary insertion, avoiding thus any kind of post-syntactic operation.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".