Perceptions of variation in second-generation Montrealers' speech : methods for remote ethnolinguistic research
Bibliographic record
Abstract
This dissertation assesses if and how ethnicity plays a role in speech perception amongst French speakers in Montreal, Quebec. Is ethnolinguistic variation present, and is it noticeable to Montrealers? In so doing, this work highlights the conflicting nature of two bodies of work: ethnographic and cultural studies research on immigrant communities in Montreal and sociolinguistic research on the region. The former underscores the importance of ethnic and cultural heritage in second-generation speakers’ self-presentation and speech, while the latter assumes that these same speakers have uniformly assimilated to a regional norm. For this dissertation, I aimed to collect and analyze data to better adjudicate between these hypotheses. As such, I created a new corpus, featuring women from the three largest ethnic / cultural communities in Montreal: Haitian, North African, and Quebecker, and experimented with techniques for running an exploratory perceptual experiment remotely. This study speaks to (i) methods of recruitment for remote sociolinguistic interviews, (ii) methods of conducting experiments online, (iii) techniques used in the free classification approach to perception tasks (Clopper & Pisoni, 2007), (iv) how second-generation Montrealers’ speech is perceived, and (v) why disciplines contradict each other with regard to these communities.
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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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 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) | 0.009 | 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".