Trends of replication studies in Applied Linguistics journals: A systematic review over half a century
Bibliographic record
Abstract
Despite the importance of replication research in scientific fields, very few replications are conducted in applied linguistics (AL). To enhance language researchers’ awareness of replications and provide a systematic evaluation of current replications, this study analyzed replication studies published in 92 AL leading journals from 1970 to 2021 based on five themes of replication labels, methodological orientations, research trends, authorship, and citation counts of replicators. The results reveal that replication labels have explicitly been mentioned since 2002, the replication of quantitative studies has predominately been raised, studies on second language acquisition were frequently replicated, collaborative authorship has increased in replications, and influential AL scholars tend to conduct replication research. The study highlights the need for a well-established replication classification and calls for replication research in the areas and methodological orientations marginalized in AL. It is also recommended that prominent figures perform more replication research to consolidate its status in AL.
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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 | 0.156 | 0.449 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.032 | 0.042 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".