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Enregistrement 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 sur OpenAlexaboutno aff

Notice bibliographique

RevueUnderline Science Inc. · 2021
Typeother
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHomelandVariation (astronomy)Heritage languageContext (archaeology)SociolinguisticsConversationCultural heritageSample (material)

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,360
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,037
Tête enseignante GPT0,315
Écart entre enseignants0,279 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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