Un cadjin qui dzit bon dieu! : assibilation and affrication in three generations of Cajun male speakers
Bibliographic record
Abstract
More often than not, the linguistic research of Cajun French rests primarily at the morphological and syntactic level or focuses on aspects of culture and identity. It was thus my goal here to examine Cajun French at the phonological level. More specifically, I examined two phonological phenomena in Cajun French: assibilation and affrication. Both of these features may result when the dental consonants /t/ and /d/ precede either of the high vowels /i/ and /y/. Under these constraints, therefore, words such as petit (“small”) and dire (“to say”) are pronounced as [pitsi] and [dzir] when assibilated and [pitʃi] and [dʒir] when affricated. Affrication of dental stops is a well-attested feature of Acadian French in Canada and is a purported feature of Cajun French, while a high rate of assibilation is common in Quebec French. Assibilation, furthermore, is rarely mentioned when discussing Cajun French. I used recorded interviews of 60 individuals from the Cajun French corpus, created by Dubois in 1997, to analyze the presence and variation of these features in four Louisiana parishes. My first goal was to determine where in Louisiana one finds these features. Secondly, I analyzed which linguistic factors affect assibilation and affrication. I found that voicing context plays a role in determining variant production in certain settings, particularly with assibilation. For affrication, I found that syllable position is actually an indicator of lexicalization in Cajun French. Nowhere is this lexicalization more evident than in the categorical affrication of cadien (“Cajun”). Thirdly, I examined the effects of certain social factors on variant usage. Results showed that gender affects variant use, with women generally preferring the occlusive ‘norm’ while men demonstrated greater variation. Location was the most significant factor to the production of both assibilation and affrication. St. Landry and Avoyelles had higher rates of both features than Lafourche and Vermilion, for example, where the features were extremely rare. Finally, variant rates increased among younger speakers despite an overall attrition and leveling of Cajun French occurring in these communities due to language shift and language death.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".