Acoustics and ice hockey: The sociophonetic impact of Canadian English on American-Born players
Bibliographic record
Abstract
My research utilizes sociophonetic analysis to document the linguistic identity construction process that is ongoing in the sport of ice hockey. I argue that American-born players are constructing a hockey-based identity influenced by Canadian English (CE) due to the historical Canadian dominance of the sport. This identity incorporates Canadian Raising, FACE and GOAT monophthongization, both commonly attributed to CE and largely unexplainable based on players’ regional dialects, and altered vowel production in hockey-specific terminology, most notably in the word hockey itself, unique to the hockey community. To document this variation, I analyze vowel formant values taken from sociolinguistic interviews with professional hockey players. I assess F1 and F2 values throughout vowel durations to establish if players are converging in production away from regional dialectal variants towards shared hockey-based variants. I argue these variants have gained indexical value linked to an emerging hockey-based identity that, although influenced by CE, is unique to the hockey community. In ongoing research, I aim to further document that this variation is most evident in hockey-specific terminology and that lexical diffusion occurs outwardly from these terms over time leading to players developing a more prevalent hockey-based identity as the sport gains more importance in their lives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".