Open Science in eating disorders: Using current evidence to inspire a plan for increasing the transparency of our research
Bibliographic record
Abstract
OBJECTIVE: There is increasing consensus that open science practices improve the transparency and quality of clinical science. However, several barriers impede the implementation of these practices at the individual, institutional, and field levels; understanding and addressing these barriers is critical to promoting targeted efforts in increasing effective uptake of open science. METHODS: Within this research forum, we drew from publicly available online information sources to identify initial characterizations of researchers engaged in several types of open science practices in the field of eating disorders. We use these observations to discuss potential barriers and recommendations for next steps in the promotion of these practices. RESULTS: Data from online open science repositories suggest that individuals using these publishing approaches with pre-prints and articles with eating-disorder-relevant content are predominantly non-male gender identifying, early to mid-career stage, and are more likely to be European-, United States-, or Canada-based. DISCUSSION: We outline recommendations for tangible ways that the eating disorder field can support broad, increased uptake of open science practices, including supporting initiatives to increase knowledge and correct misconceptions; and prioritizing the development and accessibility of open science resources. PUBLIC SIGNIFICANCE STATEMENT: The use of open science practices has the potential to increase the transparency and quality of clinical science. This Forum uses publicly sourced online data to characterize researchers engaged in open science practices in the field of eating disorders. These observations provide an important framework from which to discuss potential barriers to open science and recommendations for next steps in the promotion 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 | 0.529 | 0.695 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.009 | 0.040 |
| Scholarly communication | 0.039 | 0.093 |
| Open science | 0.011 | 0.032 |
| Research integrity | 0.020 | 0.035 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".