A corpus for studying sociolinguistic variation in Italian in migratory settings: homeland and heritage comparisons
Bibliographic record
Abstract
Much has been observed about the types of changes in heritage languages (HLs, varieties spoken in a minority context outside the homeland of a language). Work conducted in an experimental framework has reported primarily simplification, attrition and incomplete acquisition. The Heritage Language Variation and Change (HLVC) corpus, in contrast, permits examination of HLs as produced spontaneously in more relaxed conversational contexts. Through this methodology, we find a greater balance between retention of homeland patterns and variation suggesting change over time. Here we focus on the Italian data from the HLVC corpus, which has data from 10 languages. All languages have been sampled and archived in the same way. Recordings of conversational speech are available for four groups of speakers: homeland and three successive generations of heritage speakers (Gen1, Gen2, Gen3). The homeland Italian speakers have always lived in Calabria, Italy, and were recorded in conversation with other Calabrese speakers in 2013 in Calabria. Gen1 speakers were also born and raised in Calabria until at least age 18 but subsequently have lived for at least 20 years in Toronto. Gen2 speakers were born in Toronto (or arrived before age 6), and their parents qualify as Gen1. Gen3 speakers were all born in Toronto and their parents qualify as Gen2. Heritage speakers were recorded between 2009 and 2019 in Toronto. The target is 40 heritage and 12 Homeland speakers per language, to provide a sample distributed across genders and age groups in each generation. All data was collected and analyzed following the standard Labovian sociolinguistic interview protocol (Labov 1984). All interactions were initiated and recorded in Calabrese Italian. In addition, participants respond to an Ethnic Orientation Questionnaire (describing their language use preferences and practices, social network and ethnic orientation) and complete a brief picture description task. Instruments and further methodological details are available in Nagy (2009, 2011, 2015). From the conversational speech transcribed in ELAN (Wittenburg et al. 2006), a number of sociolinguistic variables have been examined from a variationist sociolinguistic perspective. This consists of coding many/all tokens of the variable for relevant contextual features and for social attributes of the speaker who produced each and then conducting multivariate analyses (Mixed Effects Models) to see the relative size and direction of effect of each factor. Here we focus on the linguistic features of null subject and VOT (voiceless stop aspiration) (Nagy 2015, Nodari, Celata & Nagy 2019). The two variables are interesting for distinct reasons: null subject is a major element of morphosyntactic differentiation between English and HL, whereas voiceless stop aspiration, which is present as a phonetic feature in both languages, has socio- indexical value in homeland Calabrian speech, particularly in unstressed syllables. Based on HLVC data, we show that rate of presence/absence of null subject pronouns does not differ significantly by generation since immigration or from available homeland comparators, thus questioning the view that attrition or incomplete acquisition are prevalent in the heritage language contexts. HL speakers also show no evidence of VOT lengthening in stressed syllables as a consequence of contact with English; in contrast, they do show progressive de-aspiration of unstressed syllables, particularly in the shift between Gen 2 and Gen 3.Moreover, some cross-generational changes are non-linear, as Gen3 speakers reproduce some of the patterns attested in Gen 1 speech. We will discuss these data and highlight the importance of corpus-based sociolinguistic methodologies for the study of variation and change in migratory settings. References Labov, William. 1984. ‘Field methods of the project on linguistic change and variation’. In Baugh, John / Sherzer, Joel (eds.). Language in Use: Readings in Sociolinguistics. Englewood Cliffs, Prentice Hall: 28–53.<br> Nagy, Naomi. 2009. Heritage Language Variation and Change. http://projects.chass.utoronto.ca/ngn/HLVC/.<br> Nagy, Naomi. 2011. ‘A multilingual corpus to explore geographic variation’, Rassegna Italiana di Linguistica Applicata 43(1-2): 65-84.<br> Nagy, Naomi. 2015. ‘A sociolinguistic view of null subjects and VOT in Toronto heritage language’, Lingua 164B: 309-327.<br> Nodari, Rosalba / Celata, Chiara / Nagy, Naomi. 2019. ‘Socio-indexical phonetic features in the heritage language context: VOT in the Calabrian community in Toronto’. Journal of Phonetics 73: 91-112. <br> Wittenburg, Peter / Brugman, Hennie / Russel, Albert / Klassmann, Alex / Sloetjes, Han. 2006. ELAN: A Professional Framework for Multimodality Research. In Calzolari, Nicoletta / Choukri, Khalid / Gangemi, Aldo / Maegaard, Bente / Mariani, Joseph / Odijk, Jan / Tapias, Daniel (eds.). Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06): 1556-1559.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".