The imbalanced interaction of verbal ambiguity and pro-drop: The functional hypothesis in homeland and heritage varieties of Calabrian Italian and Ciociaro
Notice bibliographique
Résumé
In languages like Italian and Spanish, verbal inflection is often able to disambiguate the subject for person and number. These languages also permit a null subject pronoun (pro-drop). In other Romance languages, such as French, verbal paradigms are much more syncretic, and overt subject pronouns are required in most instances.\nThe functional hypothesis proposes a causal relationship between these two aspects of a language: if a verb’s inflection disambiguates, an overt subject can be redundant.\nIn this dissertation, I investigate pro-drop in Ciociaro—a sibling language of Italian, which is spoken in Frosinone, Italy. Ciociaro’s verbal inflections are highly syncretic compared to Italian. I compare Ciociaro to the regional Italian spoken in Calabria, which has a more distinctive verbal paradigm.\nIn comparing the significant factors that constrain pro-drop in these two languages, if the functional hypothesis is supported, Ciociaro should employ overt subject pronouns more than Calabrian Italian to compensate for its more ambiguous verb phrases.\nI use three datasets in this dissertation: a heritage corpus of interviews I recorded with the Ciociaro community of Sarnia, Ontario (CHILS Corpus); a homeland Ciociaro corpus drawn from a linguistic atlas containing elicitations (AIS; Jaberg & Jud, 1928); and a Calabrian Italian corpus of recorded interviews, both the heritage community in Toronto and the homeland variety spoken in Calabria (HLVC Corpus; Nagy, 2011). From these corpora I extract 100 tokens from each speaker, which are then coded for linguistic and extralinguistic variables that are significant in comparable pro-drop analyses. Multivariate analyses are then conducted using Rbrul (Johnson, 2009) to support comparisons of the factors’ effects in each corpus.\nThe CHILS corpus comprises 1,736 tokens from 20 speakers, with a 72% null subject rate. The significant variables are: the phrase’s subject (subject), priming from the previous realization, and verb tense.\nFrom the AIS there are 583 tokens from six participants, with a 76% null rate. The significant variables are: priming from the elicitation prompts, subject, and ambiguous, meaning the verbal inflection is ambiguous for subject.\nThe HLVC corpus contains 1,634 tokens from 20 speakers, with an 80% null rate. The significant variables are: priming from the previous referent, subject, and preverbal elements.\nIn a combined analysis of all three corpora, subject and corpus are the significant variables, with CHILS disfavouring and HLVC favouring null subjects. This supports the functional hypothesis as Ciociaro has a higher rate of ambiguous verbal inflections. The significance of subject is consistent with other pro-drop research suggesting a pan-Romance effect.\nThe combined analysis also reveals that ambiguous is not a significant variable. Yet, 34% of the CHILS tokens have an ambiguous subject, while it is only 7% for the HLVC corpus. While ambiguous phrases are inversely correlated with null subject rates across the datasets, they are not significantly causally linked. Thus, the functional hypothesis is not supported in this respect.\nThis dissertation advances our understanding of pro-drop in Romance languages, representing the first variationist analysis of pro-drop in an Italo-Romance dialetto.
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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 | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,005 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».