Reproducible research practices and transparency across linguistics
Bibliographic record
Abstract
Scientific studies of language span across many disciplines and provide evidence for social, cultural, cognitive, technological, and biomedical studies of human nature and behavior. By becoming increasingly empirical and quantitative, linguistics has been facing challenges and limitations of the scientific practices that pose barriers to reproducibility and replicability. One of the proposed solutions to the widely acknowledged reproducibility and replicability crisis has been the implementation of transparency practices, e.g. open access publishing, preregistrations, sharing study materials, data, and analyses, performing study replications and declaring conflicts of interest. Here, we have assessed the prevalence of these practices in randomly sampled 600 journal articles from linguistics across two time points. In line with similar studies in other disciplines, we found a moderate amount of articles published open access, but overall low rates of sharing materials, data, and protocols, no preregistrations, very few replications and low rates of conflict of interest reports. These low rates have not increased noticeably between 2008/2009 and 2018/2019, pointing to remaining barriers and slow adoption of open and reproducible research practices in linguistics. As linguistics has not yet firmly established transparency and reproducibility as guiding principles in research, we provide recommendations and solutions for facilitating the adoption of these practices.
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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 | 0.729 | 0.868 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.011 | 0.039 |
| Scholarly communication | 0.029 | 0.035 |
| Open science | 0.009 | 0.023 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".