Bibliographic record
Abstract
Adjectives appear predominantly postnominally in Spanish, and when prenominal, cannot be interpreted as restrictive. We explore whether heritage speakers of Spanish have the same interpretive and ordering restriction as monolinguals. Twenty-two US college-age heritage speakers and 17 college-age monolinguals from Peru completed a rating task that manipulated word order and interpretation. Items varied in word order (Adj-N/N-Adj) and interpretation (restrictive-only, color and nationality adjectives, and ambiguous adjectives, restrictive and non-restrictive), all framed within a context that favored a restrictive interpretation. Both groups judged Adj-N orders lower than N-Adj orders, and restrictive adjectives lower in prenominal position than ambiguous adjectives. Consequently, we argue that heritage speakers (HS) have the relevant knowledge regarding word order and interpretation, and the interactions among the two properties. We propose a syntactic representation involving NP-raising for both groups, and suggest that in some cases, the higher copy of the NP is deleted, resulting in the linear order Adj-N. We also argue that this analysis may explain the range of individual variation across heritage speakers.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".