Hybridité et variation dans les SMS : Le corpus Texto4Science et l’oralité en français montréalais
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This article presents an analysis of a Montreal French corpus of text messages and considers the link between text messaging, and both spoken and written language. This corpus is part of a larger corpus of text messages sent by mobile phone (Texto4-Science). Our study focuses on two morphosyntactic variables for which an important sociostylistic variation has been reported in Montreal French: the alternation between the strong pronouns nous/nous autres ‘ we/us’ (as non clitics), and between the subject clitics on/nous ‘ we’. Their comparison in the text messages corpus and in spoken corpora shows that while text messages tend to approximate spoken language, they are not a perfect reflection of it. Generally, the hybridity of text messages can be conceived in the following manner: text messages obey a double standard (spoken and orthographic) and allow for occasional transgressions (formal markers associated with the written language and nonstandard spelling reflecting the spoken language).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it