Restrictions on ordering of adjectives in Spanish
Bibliographic record
Abstract
Abstract. Sequences of multiple modifying adjectives are subject to poorly understood lexical ordering restrictions. There are certain commonalities to these restrictions across languages, as well as substantive language variation. Ordering restrictions in Spanish are still under empirical debate, with some proposing strict ordering for direct modifier adjectives; others proposing broad ordering restrictions based on the contrast between intersective and non-intersective adjectives, and yet others raising the possibility that adjectival order is fully unrestricted. The goal of the present study is to examine corpus evidence for adjectival sequences. We look at both sequences of two postnominal adjectives (Noun +Adjective + Adjective, NAA sequences) as well as sequences of one prenominal, and one postnominal adjective (Adjective + Noun +Adjective, ANA sequences). The results from the NAA datasets clearly categorically confirms that relational adjectives are structurally closer to the noun. There is some evidence for an ordering bias along the line of the intersectivity hypothesis, but little else in term of hard evidence for restrictions. Additional ordering constraints appear once we incorporate the ANA datasets into the empirical picture. One interpretation is that these restrictions can be subsumed under an approach where evaluative adjectives have to occupy the prenominal restriction. In sum, the evidence is most compatible with the middle ground approach, but not with a fully articulated set of ordering restrictions.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".