The imbalanced interaction of verbal ambiguity and pro-drop: The functional hypothesis in homeland and heritage varieties of Calabrian Italian and Ciociaro
Bibliographic record
Abstract
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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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".