Technologies and Sociomaterial Tensions in the Second Language Classroom
Bibliographic record
Abstract
This exploratory study was inspired by the question “what do digital tools (un)expectedly produce in the second language classroom?” Examining the experiences of two focal French as a Second Language teachers, this article uses sociomaterial theories to explore how the confluence of students, teachers, technologies, and other materials produced disruptions and new possibilities for language learning. The analysis, presented through vignettes of lessons involving Scratch coding and WordReference.com, maps these tensions and the ways the various actors respond to the (un)expected outcomes. These moments reinforce that technology integration in language learning is not simply a manifestation of the teacher's pedagogical planning, but an unfolding with multiple possible becomings and unanticipated trajectories. This article contributes to the growing corpus of studies employing sociomaterial approaches and considers the importance of other materialities in understanding the role of technology in language learning.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.018 | 0.032 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".