MétaCan
Menu
Back to cohort
Record W6966640187 · doi:10.48448/1pyw-3k36

A corpus for studying sociolinguistic variation in Italian in migratory settings: homeland and heritage comparisons

2021· other· en· W6966640187 on OpenAlexaboutno aff

Bibliographic record

VenueUnderline Science Inc. · 2021
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsHomelandVariation (astronomy)Heritage languageContext (archaeology)SociolinguisticsConversationCultural heritageSample (material)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.315
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueUnderline Science Inc.French-language works237,207