Bibliographic record
Abstract
It is always enlightening to read work on translanguaging when it is done with people's own societal epistemologies.This is the case of the present issue of Lidil, focused on pratiques translangagières, which teaches us much about theories of language that in my speaking-English world I have called translanguaging (García & Wei, 2014; García et al., 2021;Otheguy et al., 2015Otheguy et al., , 2019)).The authors from the English-speaking world have studied pratiques translangagières in very different French-speaking national contexts-France, Switzerland and Canada.Each context has its own challenges and passions.In some, these practices are found in foreign and second language classrooms; in others, in bilingual education, some with immersion.Yet in others, these practices are found in the school use of what are considered dialects.There are examples of pratiques translangagières in école maternelle, among élèves entre 6-8 ans et 11-14 ans, and in the formation des enseignants.The use of pratiques translangagières are found when teaching language, math, science.And they are found among élèves, as well as enseignants.Quelles sont alors les pratiques translangagières ?What do these authors and French-speaking scholars mean by pratiques translangagières?And what type of learning and development do these engender?The authors in this collection provide some definitions.Auger and Pépiot tell us that it consists, among other things, of "passer d'une langue à l'autre"; Guérif, Savoy, and Dango tell us that it is to mobilize "deux ou plusieurs langues dans la construction et l'appropriation de savoirs et compétences disciplinaires".And Dall'Aglio, Fonseca, Favre, Gajo, and Vaissiere tell us that it consists of "la présence de plus d'un langage, dialecte ou registre".Indeed, this use of two or more languages is the way in which translanguaging practices have been studied in much education research.And this collection contributes much to these understandings.I start first by pointing out the Translanguaging Décloisonnement: An Epilogue
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".