Multiple Focus Strategies in<i>Pro</i>‐Drop Languages: Evidence from Ellipsis in Spanish
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 for pro subjects. Overt full DPs in subject position are marked, and the presence of overt full DPs is used to indicate focus on the subject (the pro ‐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 the pro ‐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 versus pro subjects. The paper also establishes links with the processing of ellipsis in other languages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 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 it