Narrative Inquiry in Language Teaching and Learning Research (Second Edition)
Bibliographic record
Abstract
Within the diverse range of methodological choices available to researchers, especially those who find their research orientation leaning towards the qualitative research paradigm, narrative inquiry has emerged as a significant yet understated focus for 21st-century applied linguists, undergraduate students, and postgraduate scholars working on their investigations into language teaching and learning experience. This seemingly new methodological development in applied linguistics has drawn its inspiration primarily from sociological and psychological literature (Barkhuizen, Benson, & Chik, 2014) and thanks to the inspiring work of Connelly and Clandinin (1990) and has eventually transitioned into many other domains, including social sciences and humanities (Connelly & Clandinin, 1990; Lieblich, Tuval-Mashiach, and Zilber, 1998; Pinnegar & Daynes, 2007; Riessman & Speedy, 2007; Webster & Mertova, 2007), even though narrative inquiry has been practiced before (Clandinin & Rosiek, 2007). With the increasing scholarly contributions and published empirical research, narrative inquiry has evolved into a vital and multifaceted methodology that, while rooted in sociological and psychological foundations, has gained widespread recognition across various disciplines, particularly in applied linguistics, as an optimal investigative route for exploring lived experience of language teaching and learning.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".