Differential Object Marking in Levantine Arabic
Notice bibliographique
Résumé
This dissertation investigates the phenomenon of Differential Object Marking (DOM) in Northern Galilee Arabic (NGA). DOM is a widespread linguistic phenomenon in which certain objects of verbs appear in a different form from the expected one depending on various factors. While DOM has been well studied cross-linguistically, it has been less investigated in Arabic and in particular NGA. The thesis provides a detailed investigation of DOM in NGA which is complemented by novel arguments about the syntax, semantics and pragmatics of DOM. I demonstrate that DOM in NGA has the following properties: (i) the object needs to be an individuated definite DP, (ii) the DP is marked by a prepositional dative, (iii) the dative marked DP is accompanied by clitic doubling i.e., the clitic and the marked DP co-refer, and (iv) the marked DP is interpreted as an aboutness topic. I argue that DOM is derived by rightward A-movement to the edge of vP, specifically to Spec-ApplP. I provide three pieces of evidence for this argument: (i) island tests, (ii) binding, and (iii) adverbial placement tests. The empirical motivation for movement to ApplP is primarily for case checking. The movement is accompanied by clitic doubling, where the DP object and the corresponding clitic start the derivation by forming a big-DP. The DP object is differentially marked by movement to Spec-ApplP where the Appl head assigns dative case to it. Another novel motivation for the DOM movement to ApplP comes from aboutness topicality. I argue that the property that distinguishes DOM structures in NGA from their non-DOM counterparts is aboutness topicality (Reinhart 1981). I argue that the obligatory movement of the differentially marked object to the phase edge of vP allows its anchoring to a referential address, making it an aboutness topic. I further observe that only nominals that can be mapped onto a referential address (Endriss 2009) can be differentially marked. Consequently, quantifier phrases can also be DOM but only if they can be mapped onto a minimal witness set. This dissertation also explores which types of nominals can participate in DOM. Based on Zarka and Hacohen’s (2023) experimental work, I show that only highly atomic nominals in Grimm’s (2012) sense can be differentially marked. I further demonstrate that these nominals are mappable onto a referential address and are able to hold an anaphoric relation with a clitic. The big-DP, i.e., the DP and the corresponding clitic which form a single unit, accurately predicts that elements that are mappable onto a referential address are able to be linked to a pronoun. I argue that the DOM structure is generated only if the nominal is merged as part of the big-DP. However, the syntactic analysis does not fully account for the overall distribution of DOM. I demonstrate that DOM has certain pragmatic properties, which raises the question of how they arise. Adopting Kučerová and Zarka (in prep), I argue that DOM in NGA functions as an illocutionary marker that grammatically marks the asserted proposition as a non-default Discourse Commitment (Gunlogson 2001; Farkas & Bruce 2010) giving rise to a range of speech acts including emotive content. It is suggested that the obligatory illocutionary properties of DOM stem from structural economy. Since DOM involves an additional structure (clitic doubling and an applicative projection), which is absent in non-DOM counterparts, this additional structure triggers interpretive effects that would not be available otherwise (e.g., Fox 2000; Sichel and Wiltschko 2021). The analysis put forward in this thesis is specific to NGA. It is an open question whether it extends to other languages with DOM. Similar to NGA, DOM in languages like Romanian (e.g., Hill & Tasmowski 2008) and Catalan (e.g., Escandell-Vidal 2009) is associated with discourse pragmatic effects, but further research is required for other languages. The proposed analysis of DOM raises the question of whether a uniform analysis of DOM is possible by unifying the different DOM systems as instantiations of economy-driven interpretive effects.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».