Bibliographic record
Abstract
This paper looks at EPP and word order in Arabic in light of Chomsky’s Labeling Theory, proposed in POP and POP Extensions. In the current framework, EPP—the principle that SpecTP must be filled in—is eliminated. EPP-driven movement is reduced to labeling failure: if T fails to label the structure that arises after E-merge of the subject in Spec of vP [DP vP], then filling SpecTP becomes necessary in order to ‘strengthen’ (the labelability of) T. Chomsky postulates two types of T: Strong and weak. English-type languages, which show poor agreement inflection, have a weak T, and therefore impose the Fill-SpecTP requirement. On the other hand, NSLs, Chomsky claims, have a strong T which can label the TP structure, by virtue of having rich agreement inflection. This paper shows that Chomsky’s approach to EPP makes wrong predictions. Instead, a freezing effect account which also maintains a labeling system can explain the word order facts in Arabic. Crucially, the account proposed does not make resort to Chomsky’s parameter of strength or otherwise of T.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".