Open science in sport and exercise psychology: Review of current approaches and considerations for qualitative inquiry
Bibliographic record
Abstract
Open science practices including open access (OA) publication, open methods, study preregistration, and open data are gaining acceptance across diverse fields of research. These practices are promoted as strategies to improve the reproducibility of research findings and the replicability of studies to accumulate knowledge and advance science. However, these arguments may raise concerns for qualitative researchers, and open science practices pose several challenges for qualitative researchers. The purpose of this paper is: (1) to review the state of open science practices within sport and exercise psychology, and (2) to discuss the implications of open science for qualitative inquiry. We examined open science practices across quantitative and qualitative articles in 11 sport and exercise psychology journals. While OA publication is a relatively recent phenomenon, OA articles were cited slightly more often than non-OA articles, although this difference was not significant. Some researchers provided supplementary materials alongside published articles, but researchers do not appear to be openly sharing the methods and data from their studies. No articles were published as preregistered studies at the time of our review. Some benefits of open science practices for qualitative inquiry include transparent documentation of the research process, opportunities for collaborative and pluralistic analyses, access to data across multiple research sites and from difficult-to-access settings and participants, and opportunities for teaching qualitative inquiry. We conclude by addressing several key questions including participant consent, confidentiality and anonymity, analyzing de-contextualized qualitative data, storing and accessing data, study preregistration, and the principle of emergent design within qualitative inquiry.
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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 | 0.238 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.020 | 0.026 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.006 | 0.007 |
| 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; 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".