Show Me the Data, Jerry! Data Visualization and Transparency
Bibliographic record
Abstract
It is debatable whether or not science is progressive.1 Evidence of “p-hacking” and scientific bias exists.2,3 However, we can increase the likelihood that science remains or becomes progressive by increasing transparency and using practices that reduce the chance of scientific errors, such as unsound interpretation of data. Specifically, we would like to discuss the importance of data visualization and data transparency, an area of great evolutionary need in our expectations of contributions to the International Journal of Sports Physiology and Performance (IJSPP). Over the years, many fields have highlighted the importance of improving how scientists present data. In 2015, Weissgerber et al4 noted many issues in data visualization present in the top physiology journals after reviewing over 700 published articles. The recommendation to “encourage more complete presentation of data” is equally or possibly even more important for journals like IJSPP, where studies with small sample sizes are often published, such as those including an elite athlete population. Further, readers interested in studies that focus on the elite athlete are often interested in individual performance or n = 1 analysis alongside the performance of a team or the group response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.352 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.177 | 0.070 |
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".