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
1. Foreword (by Crystal, David) 2. Introduction (by Cougnon, Louise-Amelie) 3. Articles 4. Seek&Hide: Anonymising a French SMS corpus using natural language processing techniques (by Accorsi, Pierre) 5. SMS experience and textisms in young adolescents: Presentation of a longitudinally collected corpus (by Bernicot, Josie) 6. Automatic or Controlled Writing?: The Effect of a Dual Task on SMS Writing in Novice and Expert Adolescents (by Combes, Celine) 7. Development of SMS language from 2000 to 2010: A comparison of two corpora (by Kirsten-Torrado, Ursula) 8. Texto4Science: A Quebec French database of annotated text messages (by Langlais, Philippe) 9. SMS communication as plurilingual communication: Hybrid language use as a challenge for classical code-switching categories (by Morel, Etienne) 10. French text messages: From SMS data collection to preliminary analysis (by Panckhurst, Rachel) 11. A sociolinguistic analysis of transnational SMS practices: Non-elite multilingualism, grassroots literacy and social agency among migrant populations in Barcelona (by Sabate Dalmau, Maria) 12. Negation marking in French text messages (by Stark, Elisabeth) 13. i didn't spel that wrong did i. Oops: Analysis and normalisation of SMS spelling variation (by Tagg, Caroline) 14. Lol, mdr and ptdr: An inclusive and gradual approach to discourse markers (by Uygur-Distexhe, Deniz) 15. Index
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it