Multiple Focus Strategies in<i>Pro</i>‐Drop Languages: Evidence from Ellipsis in Spanish
Bibliographic record
Abstract
Abstract In this paper I use the case of Spanish to argue that language regularities may give rise to multiple strategies for marking focus. In addition to the well‐known observation that focus can be marked by word order and intonation, I present experimental results regarding ellipsis that show that overt full DPs in subject position in Spanish are marked as focused. This strategy is linked to a language regularity, namely the availability of silent (pro) subjects. In Spanish, there is a preference forprosubjects. Overt full DPs in subject position are marked, and the presence of overt full DPs is used to indicate focus on the subject (thepro‐drop hypothesis). I will provide a novel syntactic analysis of the ellipsis structures, discuss discourse licensing conditions and present two experiments that investigate preferences in ellipsis resolution and argue in favor of thepro‐drop hypothesis. Experiment 1 compares structural preferences for ellipsis resolution across bare argument ellipsis and replacives, investigating the role of syntax and the information‐structure status of the subject. Experiment 2 compares the resolution of ellipsis with antecedents with overt DP subjects versusprosubjects. The paper also establishes links with the processing of ellipsis in other languages.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".