El 'voseo mixto verbal' de hablantes chilenos en Montreal: Estudio de caso en un contexto de contacto dialectal VERBAL VOSEO AMONG CHILEAN SPEAKERS IN MONTREAL: A CASE STUDY IN A DIALECT CONTACT SITUATION
Bibliographic record
Abstract
La presencia de numerosos hispanohablantes, de diversos orígenes, en la ciudad canadiense de Montreal suscita una situación de contacto lingüístico entre distintas variedades del español. A pesar del interés que esta situación ofrece para la lingüística hispanoamericana, hasta donde tenemos constancia, no ha habido estudios sobre los posibles efectos del contacto dialectal sobre el español de migrantes de primera y segunda generación en Montreal. Precisamente en esta línea de trabajo, el presente artículo pretende ser una primera contribución a este campo de estudio, en concreto sobre el español de migrantes chilenos. De este modo, y gracias a un corpus oral de conversaciones libres, se propone analizar qué ocurre con el fenómeno del voseo mixto verbal en el habla de un grupo de informantes de ese origen. Due to the presence of several groups of Spanish speakers of various backgrounds, a language contact environment exists among different varieties of Spanish in the Canadian city of Montreal. Despite the possibilities that this situation offers to the field of American Spanish Linguistics, no study, as far as we know, has been undertaken to determine the effects of dialect contact on first- and second-generation migrants' Spanish. With this article, we aim to make the first contribution to this area of study; in particular, we focus on the Spanish of Chilean migrants. Using a corpus of naturally-occurring conversations, we analyze the verbal voseo feature in the speech of these migrants.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".