Apparent phonetic approximation: English loanwords in Old Quebec French
Bibliographic record
Abstract
A key debate in loanword adaptation is whether the process is primarily phonetic or phonological. Is it possible that researchers on each side are viewing equally plausible, but different, scenarios? Perhaps, in some language situations, adaptation is carried out mainly by those without access to L2 phonology and is, perforce, perceptually driven. In other situations, adaptation may be done by bilinguals who actively draw upon their knowledge of L2 phonology in adapting loanwords. The phonetic strategy would most likely be favored in situations where the vast majority of the population did not know the L2, thus having no possible access to the L2 phonological system. The phonological strategy, on the other hand, is most likely to be favored in situations where there is a high proportion of speakers who are bilingual in the L1 and L2. This possibility is tested by comparing the adaptations of English loanwords in 19th- and early 20th-century Quebec French, when bilinguals were few, to those of contemporary Quebec French, in which the rate of bilingualism is far higher. The results show that even when the proportion of bilinguals in a society is relatively small, they determine how loanwords are pronounced in the borrowing language. Bilinguals adapt loanwords on the basis of phonology, not of faulty perception of foreign sounds and structures. However, in a society where bilinguals are few, there is a slight increase in non-phonological influences in loanword adaptation. We address the small role played by non-phonological factors, including phonetic approximation, orthography, and analogy (true or false), showing that false analogy, in particular, may give the impression that phonetic approximation is more widespread in a loanword corpus than is actually the case.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".