Broken plurals and (mis)matching of ɸ-features in Tunisian Arabic
Bibliographic record
Abstract
Abstract The aim of this paper is to explain an unusual agreement pattern that arises between Tunisian Arabic broken plurals and their targets. For example, a verb may agree with a plural subject in all ɸ -features or, rather oddly, in singular/feminine, even when the subject (the controller) is masculine plural. Developing an idea first briefly sketched—but ultimately not adopted—by Zabbal (2002), we argue that broken plurals are hybrid nouns. Hybrid nouns have been the topic of much recent research (Corbett, 2000, 2015; den Dikken, 2001; Wechsler and Zlatić, 2003; Danon, 2011, 2013; Matushansky, 2013; Landau, 2015; Smith, 2015): either their syntactic or semantic features can be the target of agreement, creating the possibility of an agreement mismatch. Using Harbour’s (2011, 2014) theory of number, coupled with some innovations, we provide the featural make-up of Tunisian Arabic broken plurals and contrast it with that of collectives, on the one hand, and sound plurals, on the other. We propose that the feminine agreement seen with broken plurals is associated with a [+ group] feature, one that is exponed as - a . In the course of the discussion, we will argue that all gender features are visible at LF (Hammerly, 2018) and that semantic agreement is routinely possible with nouns that are low on the Animacy Hierarchy.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".