Bibliographic record
Abstract
The distribution of DDPP in raising constructions –depending on the embedded clause’s formal properties– has been essential for Case Theory and movement. Likewise, the behavior of DDPP, according to agreement facts, has given rise to relevant discussions about the kind of movement involved (A-Movement/A’-Movement). Nevertheless, this distribution is not so clear in certain Spanish dialects, which shows a double agreement effects. It means that the embedded verb as well as the raising verb (parecer ‘to seem’) present inflectional number (and person) morphology: Parece-n que lo olvida-n (seem.3PL that it forget.3PL ‘They seem to forget him’). The analysis of the data in these varieties allows us to define many characteristics which are relevant from a descriptive and a theoretical point of view. Descriptively, it is possible to identify some notable particularities, with respect to the position of the DP, which triggers agreement and the interaction of these constructions with dative experiencers as well (Me parece que... ‘It seems to me that...’). From a theoretical point of view, these data have consequences for approaches on agreement, on the relationship between Case and movement, and on the discussion regarding the Experiencer Paradox in Spanish. Additionally, they allow us to identify a new empirical domain in which a DP plural number feature has an active role in the Probe-Goal domain.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".