Bibliographic record
Abstract
This article is about Newfoundland French, a variety of Acadian French spoken in the peninsula of Port-au-Port in Newfoundland (Canada). The corpus is made up of recordings of interviews made between 1981 and 1998, with 53 native speakers, all bilingual, who had had no exposure to French as a taught language, and were then aged over 40. The verb morphology of Newfoundland French (present indicative, imperfect, future and present subjunctive) is used to throw light on the more or less successful attempts at systemic regularization of a variety of French, far from normative pressure, within the theoretical framework of Chaudenson, Mougeon and Beniak (1993). Certain rules are tentatively described and it is shown, for example, that the commonest verbs are also the most irregular. The underlying tendency of Newfoundland French, under the influence of syntactic and phonetic factors, towards a system potentially presenting a single form for each verb tense, does not however eliminate the many exceptional forms, which are presented here. The multiplicity of forms encountered among the speakers seems to represent a transitory phase in language development preceding normalization. This situation is a result of the maintenance of older diachronic stages within a variety transmitted in purely spoken form up to the present day, and not to the disorder which precedes language death, as the small number of speakers may also lead one to suppose.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".