Post-Vernacular Self-Translation: Bringing Languages Back from the Brink
Bibliographic record
Abstract
Though less common and therefore less studied, this last scenario is perhaps more exciting insofar as it shows the potential of self-translation as a form of collective empowerment (and not merely a tool for individual self-promotion.) Not so long ago, speakers of fragilized or endangered languages that no longer functioned as full-fledged vernaculars in everyday life but have become «post-vernacular» (Shandler), felt that they had no choice but to become translingual writers in a “major” language. For a variety of reasons (censorship, lack of standard grammar and spelling, their own limited literacy), publishing in their native or heritage language was hardly an option. The first quarter of our century has however witnessed a revival of some of these languages, with self-translation (which Shandler does not study) sometimes playing a not insignificant role for long-neglected languages that were/are on the brink of extinction. By developing literacy, empowering minorities and visibilizing formerly invisible languages, post-vernacular self-translation becomes an exercise in inclusive democracy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".