Qualitative language education research in the past quarter century: A bibliometric analysis
Bibliographic record
Abstract
A central concern among qualitative researchers over the last two decades has been enhancing its visibility and credibility, particularly among quantitative researchers as well as general audiences. Addressing calls for top-down insights to help stakeholders of research take stock of an increasingly large and complex literature body, this bibliometric analysis provides quantitative insights into 3,758 qualitative studies in language education, published in 34 major academic journals from 1999 to 2021. It investigates patterns of research productivity across authors, their institutions and the countries these are located in, the journals authors publish in, the research approaches they use, the topics they address, and the sources they commonly cite. The study uncovered a sizeable increase in the literature body, particularly in 2019–21, driven by growing interest in staple topics relating to teaching and learning featuring predominantly case study, conversation analytic, and ethnographic methods. The literature body, starting off as largely Anglophone centric and individually authored has become more diversified. Implications largely in the form of gaps in the dataset and suggestions for future research are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.079 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.117 | 0.220 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".