Research tasks on identity in language learning and teaching
Bibliographic record
Abstract
The growing interest in identity and language education over the past two decades, coupled with increased interest in digital technology and transnationalism, has resulted in a rich body of work that has informed language learning, teaching, and research. To keep abreast of these developments in identity research, the authors propose a series of research tasks arising from this changing landscape. To frame the discussion, they first examine how theories of identity have developed, and present a theoretical toolkit that might help scholars negotiate the fast evolving research area. In the second section, they present three broad and interrelated research questions relevant to identity in language learning and teaching, and describe nine research tasks that arise from the questions outlined. In the final section, they provide readers with a methodology toolkit to help carry out the research tasks discussed in the second section. By framing the nine proposed research tasks in relation to current theoretical and methodological developments, they provide a contemporary guide to research on identity in language learning and teaching. In doing so, the authors hope to contribute to a trajectory of vibrant and productive research in language education and applied linguistics.
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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 | 0.051 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.021 | 0.027 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".