Bibliographic record
Abstract
En Tunisie, l’ordre politico-graphique est clair : la langue arabe, seule langue reconnue dans la Constitution, s’exprime par l’alphabet arabe, le français par l’alphabet latin, les chiffres servent à exprimer des grandeurs et le tunisien n’a pas de visibilité officielle à l’écrit. Les écritures des Statuts sur Facebook, en revanche, défient ces arrangements. Les limites de ces usages y sont lâches, les graphies emmêlées, les arrangements révisés et le tunisien écrit apparaît, se répand et se normalise. Je propose de comprendre ces écritures comme des expressions d’une citoyenneté horizontale engageant un processus de reconnaissance d’une langue qui n’a pas de visibilité officielle à l’écrit. Facebook devient ainsi un espace de remise en question du rôle de l’État dans sa définition d’une forme scripturaire de citoyenneté. Je soutiens, enfin, que les processus de reconnaissance ne sont pas nécessairement étayés par des pratiques de luttes et de revendications mais qu’ils peuvent se dérouler de manière relativement banale et informelle.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".