Bibliographic record
Abstract
Visualising harms: barely scratching the surface Will Stahl-Timmins data graphics designerGood visuals and graphs can quickly convey substance in a way that words cannot.Graphics make a lasting impression, are easy to digest, but hard to make.Far from being just an additional eye-catching "surface" on top of a paper, they are an integral part of how science is explained.Often, not enough attention is paid to the visual element of science communication and why it matters.I was recently involved in a project, led by Rachel Phillips, and colleagues at Imperial College London and several other UK medical schools.The project aimed to recommend graph types for reporting harms data in clinical trials.However, while we collated a few simple and easy to make graph types, the surface of this topic has barely been scratched.We need to develop and use more innovative techniques that can present data in more complex trial designs such as those with multiple outcomes and subgroups.
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 | 0.084 | 0.516 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.057 | 0.020 |
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".