Explorations du sociomatériel dans les recherches sur l’apprentissage et l’enseignement des langues
Bibliographic record
Abstract
For the last several years, we have seen a widening of the conceptual field in the research on language learning and teaching. Many articles published over the last 20 years in the The Canadian Modern Language Review draw on poststructural, sociocultural, and sociolinguistic theories and now occupy an important place alongside articles inspired by psycholinguistic perspectives. Recently, there has been interest in the field for theories of the material. Stemming from several disciplines, work on the topic emerges from various schools of thought, including posthumanism (Braidotti), new materialism (Bennett), and relational ontologies (Barad), among others, and several authors are inspired by the work of Deleuze and Guattari. According to these perspectives, discursive processes and social activities are entangled in the material world and are ontologically inseparable from it. Thus, the study of learning and teaching phenomena, inspired by these theoretical frameworks, moves away from an analysis centred solely on the person and the social. It examines the relations between the human and the material to find out how they take shape together, change continually, and affect the observed phenomena. In this article, I present some key concepts and avenues of research explored in this work. I also note how researchers explore research methods and conceptualizations of language learning and teaching that differ from those that have been privileged in the field until now.
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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 | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.012 | 0.028 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".