Cultivating the nexus of research and practice in language teacher education: A duoethnographic exploration
Bibliographic record
Abstract
This article addresses the research-practice divide in education, with a particular focus on language teacher education. In line with this special issue, we see disciplinary value in the concept of “nexus” to describe the dynamic relationship between research and practice, offering a more nuanced framework than traditional dichotomies. After introducing the notion of nexus, we briefly explore the distinction between education and education al research before outlining duoethnography as our methodology. Our central aim is to challenge the prevailing positivistic approaches to research, which prioritize causal generalizations and “what works” models, and to advocate instead for duoethnography – a more conversational and interpretive approach in support of research utilization. Duoethnography is a dialogic and iterative methodology, which enables us to juxtapose our life histories and lived experiences to interrogate our shared topic of interest. This approach generates multiple, often contested understandings of phenomena, foregrounding difference and multivocality. Through our duoethnographic analysis, we argue for the development of critical research literacy among prospective teachers to foster a sustainable and pedagogically meaningful nexus of research and practice. Rather than providing a prescriptive blueprint, we invite readers to engage actively and critically with research by embracing its complexity and interpretive nature.
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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 | 0.027 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.017 | 0.088 |
| Scholarly communication | 0.022 | 0.029 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.004 | 0.007 |
| 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".