Bibliographic record
Abstract
Aoun & Choueiri (2000) suppose the following generalization, which is an anti-locality constraint on the distribution of epithets and strong pronouns in Lebanese Arabic (LA): neither an epithet or a strong pronoun can be locally associated with a quantificationnel antecedent (QP) . They use the term local, rather than binding because they want to use this assumption to explain consistently the two scenarios that allow the binding of an epithet / strong pronoun by a QP: 1) configurations where a referring clitic occurs between the epithet / strong pronoun and the QP antecedent which itself binds the epithet / strong pronoun; and 2) configurations where an – wh operator intervenes between the resumptive element (epithet or strong pronoun) and the QP antecedent. In this paper, we criticize their analysis of anaphoric expressions since it does not account for the Jordanian Arabic (JA) data that we have highlighted (in particular, the case of doubled pronoun). We propose an alternative analysis. We will consider the non-local association configurations as local association configurations whether it is for LA or JA.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".