Rhizo-Creation of Second-Language Teachers’ Capacity for Technological Integration
Bibliographic record
Abstract
This article puts to use the work of Deleuze and Guattari to build new knowledge and understanding associated with the circumstantial nature of becoming a technology-capable language teacher through experimentations with/in the agencements of an ongoing research project associated with the design and delivery of a 12-week online graduate course in computer-assisted language learning (CALL). Methodologically, data collection encompassed participants’ assignments, semi-structured interviews, course materials, and researcher’s journal. Moreover, rhizoanalysis was deployed to map change and potentialities in teachers’ becoming. Scholarly contributions to the fields of technology, learning, and teacher education relate to re-theorizing the role and effect of human, expressive, and material elements in teacher education in CALL, as well as developing new methodologies to research micro-level singularities and emergent potentialities for teaching and learning with/in teacher education.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".