<i>Eh?</i> and <i>Hein?</i>: Discourse Particles or National Icons?
Bibliographic record
Abstract
Abstract We compare the use and function of two discourse particles that show many similarities: Canadian English eh and Canadian French hein . Surveys of anglophone students at the University of Toronto and francophone students at Université Laval reveal that these particles have similar discourse functions and that there are many parallels in their patterns of use. However, francophone students report a higher use of hein than do anglophone students of eh . Moreover, francophones have more positive attitudes towards constructions with hein than do their anglophone counterparts with respect to eh . In addition, eh —used both less often and valued less positively—has taken on additional functions as an identity marker: it is used to identify speakers of Canadian English and, in print, to evoke Canadian solidarity. In contrast, hein does not have an identity marking function. We propose that the development of an identity marking function for eh —and the lack of such a function for hein —reflects differences in how linguistic identities are constructed in English and French Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".