Comprehensibility as a Factor in Listener Interaction Preferences: Implications for the Workplace
Bibliographic record
Abstract
Changing economic realities in Canada are likely to result in increased employment opportunities for immigrant professionals. In Alberta, where these changes have already begun, issues of language in the workplace have surfaced, some relating to oral skills. In this investigation of Canadian-born employees' preferences for 40 L2 accented voices, 16 native English speakers selected a preferred voice from pairs of Mandarin- and Slavic-language-accented speech samples varying in comprehensibility. We found that comprehensibility figured importantly in their choices, as did fluency. In fact, listener preferences were influenced by comprehensibility differences of about one point on a nine-point rating scale. An additional 14 native English speakers undertook a preference task in which speech samples were presented according to degree of accent. Although this latter group tended to prefer less accented over more accented speakers, accentedness itself was less important than comprehensibility when the two groups' preferences were compared. These findings are discussed with reference to LINC (Language Instruction to Newcomers in Canada) and other ESL programming and to English in the workplace.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".