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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.012 |
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".