Bibliographic record
Abstract
I propose that the EPP is obligatory cross-linguistically, but that the forms it takes vary from language to language, and even within the same language, in some cases. Alexiadou and Anagnostopoulou (1998) argue that the EPP can be checked by either a head or by a phrasal element, and Massam and Smallwood (1997) argue that it can be checked by either a verbal or nominal element. Intersecting these two approaches leads to a total of four forms of EPP-checking, as attested, for example, in English, Niuean, Italian, Irish, and Arabic. McCloskey (1996a, 2001) argues that Irish does not have an EPP; I propose that, rather, it has an EPP which is checked by movement of the verb into Infl. Alternations within the Italian subjunctive paradigm indicate that it is the agreement morphemes which are crucial for EPP-checking in Italian. Finally, the alternations in word order and subject-verb agreement in Arabic can be explained by assuming an intra-linguistic alternation between the EPP being checked by a D-head or by a DP. Furthermore, I will argue that it is valued features, in particular, which are crucial for EPP-checking, and that the EPP seems to be related to deixis, which, as Ritter and Wiltschko (2009) argue, is characteristic of the Infl projection.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".