Beyond replication: An exact replication study of Łodzikowski (2021)
Bibliographic record
Abstract
Abstract Replication studies have become an emerging line of research in recent decades, including in computer-assisted language learning (CALL). Exact replication, which closely follows a study’s protocol, is rare as it is hard to recreate results without establishing a highly controlled environment. However, using data available online, we were able to conduct an exact replication of Łodzikowski’s (2021) study, which reported on the use of an allophonic transcription tool by 55 Polish learners of English. Allophonic features are used by native speakers to produce acoustic variants of the same phoneme. The original study offered learners an allophonic transcription tool, examined how they used it and considered its association with phonological awareness. This study extended the original research by addressing the limitations of its regression and transcription analyses. Our findings allowed us to offer several suggestions on (1) how an allophonic transcription tool can be better designed to help learners, (2) how CALL researchers can acquire more data for more useful research and (3) why more replication studies are needed in CALL.
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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 | 0.354 | 0.663 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".