Bibliographic record
Abstract
Abstract In this article, which constitutes the concluding article of the TASK Special Issue on teacher education, I reflect on the cognitive frictions that many pre-service and in-service teachers experience when they are informed about the basic principles behind task-based language teaching and are invited to work with tasks, or design them. More in particular, I describe cognitive frictions between three types of ‘knowing’ that drive teachers’ decision-making in the classroom: their personal intuitions about language learning and teaching, the theory and research-based insights they gain access to, and the data-driven knowledge that accumulates from gathering empirical data about life in their classroom environment. I will claim that the frictions that are bound to arise between those three types of knowing are inevitable, and may even be considered as inherent to the profession of (language) teaching. What is more, they can be fruitful in terms of fostering teachers’ professional growth, particularly when they get the chance to discuss those frictions with others. The communication about such frictions I will dub ‘communifriction’ in this article. Drawing on the articles included in this special issue, I describe a range of different ways in which communifriction can take shape and can become beneficial for teachers’ own professional development and for the implementation of task-based language teaching.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".