Listening to Writers and Riders: Partial Contrast and the Perception of Canadian Raising
Bibliographic record
Abstract
Listeners generally have a greater perceptual sensitivity to native contrasts compared to allophones (Whalen et al., 1997; Boomershine et al., 2008) or non-native contrasts (Goto, 1971; Sundara et al., 2006) in discrimination and other tasks. Recent research has emphasized the gradient nature of contrast, showing that many phonological relationships are intermediate or variable between contrast and allophony (Hall, 2009, 2013). This dissertation presents a series of experiments investigating the perception of what has been called marginal contrast or partial contrast using Canadian Raising as a testing ground. Experiment 1 tests discrimination of raised and non-raised diphthongs ([ʌj]~[aj] and [ʌw]~[aw]) in different phonological environments, finding better discrimination in the contrastive environment where they can create different words than in the allophonic environment where they cannot, but only for one of the two diphthongs (/aj/ but not /aw/). This diphthong difference was ambiguous—it could be a property of the diphthongs themselves, or it could have been a result of the stimuli used, specifically that [ʌj]~[aj] has more recognizable minimal pairs (e.g., writing/riding) than [ʌw]~[aw] (e.g., clouting/clouding). Experiments 2, 3, and 4 clarify this partial contrast effect and diphthong difference, finding support for an inherent diphthong difference (using non-words in Experiment 2) and for an additional effect of the minimal pairs (Experiments 3 and 4). Experiments 1b, 1c, 2b, 3b, and 4b are semi-replications of these initial four experiments. They lack an additional experimental condition that was present in the original experiments, and in each case the original partial contrast effect fails to replicate, suggesting that partial contrast effects depend on quality/quantity of linguistic exposure. Finally, Experiment 5 tests discrimination of Canadian Raising diphthongs by Canadians and Americans, finding generally faster and more accurate discrimination by Canadians, with differences between different American regions as well. Together, these experiments provide insight first and foremost into the effect of contrast—specifically partial contrast—on discrimination, as well as other topics such as cross-dialectal perception (and the effect of dialect stereotypes and dialect exposure on perception) and regional differences in the production of raising (and related phenomena) in Canada and the United States.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".