Researcher Identity, Narrative Inquiry, and Language Teaching Research
Bibliographic record
Abstract
Whereas there has been much research on language and identity with respect to learners, teachers, and teacher educators, there has been little focus on the identity of the researcher, an important stakeholder in language education. Our research therefore addresses the following question: To what extent can narrative inquiry illuminate the ways in which researcher identity is negotiated in language teaching research? To address this question, we draw on a digital literacy study in multilingual Uganda to narrate how we engaged in our own storytelling, and the process by which we invited teachers to share their experiences of teaching through the medium of English as an additional language in a poorly resourced rural school. Central themes were our attempts to reduce power differentials between researchers and teachers, and our desire to increase teacher investment (Norton, 2000) in our collaborative research project. Drawing on numerous small stories (Bamberg, 2004; Georgakopoulou, 2006), we argue that several researcher identities were realized, including international guest, collaborative team member, teacher, and teacher educator. Our article supports the case that small stories enrich traditional narrative inquiry, both theoretically and methodologically, and make visible the complex ways in which researcher identity impacts research, not only in language teaching, but in education more broadly.
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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 | 0.136 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.020 | 0.081 |
| Scholarly communication | 0.026 | 0.027 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".