Bibliographic record
Abstract
Abstract This book examines the variation found in modern spoken French, based on the research programme ‘Phonology of Contemporary French’ (Phonologie du Français Contemporain, PFC). Extensive data are drawn from around the French-speaking world, including Algeria, Canada, Louisiana, Mauritius, and Switzerland. Although the principal focus is on differences in pronunciation, the authors also analyse the spoken language at all levels from sound to meaning. The book is accompanied by a website hosting audio-visual material for teaching purposes, data, and a variety of tools for working with corpora. The first part of the book outlines some key concepts and approaches to the description of spoken French. Part II is devoted to the study of individual samples of spoken French from around the world, covering phonological and grammatical features as well as lexical and cultural aspects. The companion website provides a classroom-friendly ready-to-use multimedia version of the 17 chapters in this part, as well as a full transcription of each extract and the sound files. Part III looks at inter- and intra-speaker variation: the opening chapters provide a methodological background to the study of phonological variation using databases, while in the second section, authors present case studies of a number of PFC survey points, including Paris, the Central African Republic, and Québec. Varieties of Spoken French will be an invaluable resource for researchers, teachers, and students of all aspects of French language and linguistics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".