Bibliographic record
Abstract
2 See Dollinger (2018) for a lexicographical history of eh. 3 These early works tend to contextualize the Canadian-ness of eh to particular uses, primarily in variation with pardon.However, its standalone use as a Peircean index of Canada in popular headlines and titles, such as Orkin's (1973) humour book Canajan, Eh? (see also Gold and Tremblay 2006: 260 and Dollinger and Fee's 2017 entry for eh, sense 5) and in the commodification of the lexeme on mugs, t-shirts, and magnets since at least the 1990s (Denis 2013) suggest that from a non-linguist's perspective, the locus of this stereotype was (and is) not within eh in discourse context, but rather within the lexeme.Indeed, even early popular metadiscourse discusses eh outside of the 'pardon' function, such as in Moore's (1967) review of the Dictionary of Canadianisms on Historical Principles: "both the English and the Americans can spot a Canadian from his 'eh?' at the end of a sentence: 'It's hot, eh?"' (cited in Avis 1972: 89).Indeed, Wiltschko et al. (2018) argue that the different 'discourse functions' of eh that have been described in the literature are reducible to a core confirmational function of the lexeme.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.019 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".