An investigation into contact-induced semantic shifts in Quebec English : conciliating corpus-based vector models and variationist sociolinguistic inquiry
Bibliographic record
Abstract
This dissertation investigates contact-induced semantic shifts in Quebec English, i.e., preexisting English words which are used with a different meaning due to the potential influence of French. I propose a novel approach at the intersection of natural language processing and variationist sociolinguistics, aiming to provide a more comprehensive descriptive account as well as assess the contributions of the implemented methods.In order to conduct computational analyses of semantic variation, I created a corpus containing 78.8 million tweets from Montreal, Toronto, and Vancouver. It was used to implement different types of vector space models, i.e., computational representations of word meaning. Type-level models were used to identify new semantic shifts based on the semantic differences between Montreal and the other two cities. Token-level models were used in finer-grained analyses and allowed to further characterize their use. Despite promising results, systematic quantitative evaluation and extensive qualitative analyses suggest that these methods are hampered by noise related to their inherent characteristics as well as corpus structure.These large-scale approaches were complemented with finer-grained data collected through sociolinguistic interviews with 15 speakers living in Montreal. Varying correlations between lexical items and a range of sociodemographic factors, coupled with qualitative remarks on their use, point to four distinct patterns of synchronic variation; these in turn reflect potential diachronic processes. Interspeaker variability suggests that the use of semantic shifts is driven by speakers who tend to be younger and proficient in both English and French. The acceptability ratings are weakly correlated with computational variation measures, suggesting that they capture different dimensions of semantic variation.Overall, this dissertation has provided the first systematic description of contact-induced semantic shifts in Quebec English, and highlighted the complementarity of approaches used in different disciplines. These considerations have provided a pathway towards a better-informed use of corpus-based computational methods in studies of sociolinguistic phenomena.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".