Individual Differences as Predictors of Participation in Sound Change
Bibliographic record
Abstract
Models of sound change rely on individual listeners-turned-speakers to propagate variation which eventually turns into large-scale change. Sociolinguistic research, meanwhile, documents sound changes by aggregating data from a large number of speakers, usually disregarding individuals who deviate from the group norm. Psycholinguistic approaches to speech perception and production offer compelling evidence that individual cognitive differences are implicated in our linguistic behaviour. This dissertation aims to bring together individual difference frameworks and models of phonetically-motivated sound change to explore an instance of linguistic variation in Toronto. The production of /u/ has an allophonic distribution in which it is fronted following coronal segments and less fronted elsewhere. Post-coronal fronting has been documented as a long-term change-in-progress, while recent research indicates that the phonetic conditioning is loosening and fronting is spreading to non-coronal environments. A production experiment testing for correlations between individual cognitive differences and F2 of /u/ in two phonetic environments finds that female speakers who score low on the Empathizing Quotient scale have a significantly further back production of /u/ in the newly fronting environment, meaning that they are less innovative than their high-scoring counterparts. I argue that although this result is the opposite of the hypothesis that high-empathy women lead change, this low-empathy profile is compatible with a canonical description of a leader of sociolinguistic change. A perception experiment tests for correlations between the same cognitive factors and the perception of /u/ in coronal and non-coronal environments, to determine whether listeners at either end of the cognitive scales compensate for environment to different degrees. Women at either end of the empathizing scale do not differentiate between environments, leading to the conclusion that they are less affected by the onset than listeners who score highly on the systemizing scale. I suggest that the innovators are those whose low level of attention to detail allows them to ignore phonetic conditioning in their perception of /u/ providing the opportunity for /u/ to front in traditionally non-fronting environments, and whose low level of empathizing allows them to deviate from their peers. Both attributes are necessary to lead a co-articulatorily motivated sound change.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".