Considerations for making informed choices about engaging in open qualitative research
Bibliographic record
Abstract
There is currently little guidance that exists for researchers in the sport and exercise sciences on open qualitative research practices. The purpose of paper is to provide researchers with guidance regarding the considerations necessary for making informed decisions about engaging in open research practices within qualitative inquiry. The guidance was developed through a series of four working group meetings with experts in qualitative research and meetings with key stakeholders (study participants, journal editors, and data management experts). The wider open qualitative research literature also informed the guidance. Nine core values were first identified as underpinning the considerations for engaging in open qualitative research practices: Choice (academic freedom and participant autonomy); Plurality not replication; Flexibility and emergent design; Transparency; Relational ethics; Quality; Education; Equity; and Responsibility. Considerations for researchers are then provided in the following areas as they pertain to open science practices in qualitative inquiry: Types of Data; Types of Studies; Participant Groups; Anonymity and Confidentiality; Participant Consent; Storage and Stewardship of Qualitative Data; Knowledge Dissemination and Open Access Publications; Cost, Time, and Resources; and Preregistration of Qualitative Studies. This paper provides an initial framework for identifying considerations for engaging in open qualitative research practices. These considerations will help qualitative researchers make informed decisions about and plan for implementation of open science practices, as well as assessing the risks and benefits of open science practices in 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 | 0.644 | 0.705 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.022 | 0.051 |
| Scholarly communication | 0.030 | 0.036 |
| Open science | 0.010 | 0.028 |
| Research integrity | 0.031 | 0.034 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".