Bibliographic record
Abstract
This paper examines the unexpected verbal anti-agreement with non-human plural subjects in Standard Arabic. In this language, when the plural subject denotes non-humans, the verb fails to establish plural agreement with that subject. Non-human DPs refer to nominals which denote any animate life-form other than humans as well as all inanimate entities. In this paper, I provide two competing analyses to account for this phenomenon. In the first analysis, I build on the assumption (Mohammad, 2000) that preverbal subjects in this language are Topics and argue that the singular number marker on the anti-agreeing verb is the result of establishing partial agreement with the non-human subject in its base-position before movement/dislocation to TopP. In the second account, I borrow Corbett’s (2004) notion of ‘individuated nominals’ where it is assumed that plural nominals can either refer to collective individuals or distinct individuals; subsequently the intended referent dictates agreement on the verb. Hence, I argue that non-human plural subjects are collective nominals that are not individuated, therefore they are inherently singular and the plural marker in this case carries morphosyntactic information that does not affect the inherently imposed singular feature.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".