The Journey to a Successful Illustrated Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Illustrated review articles, rooted in scientific rigor, are made up of "capsules" or panels of visuals that together provide an up-to-date overview of a topic. Illustrated reviews aim to provide a more accessible format than traditional written reviews to facilitate more effective knowledge translation and dissemination. However, the novelty of this format can dissuade prospective authors due to uncertainty and lack of comfort. To remedy this uncertainty, we have summarized the journey of developing an illustrated review, from identifying an appropriate topic to submitting the final manuscript for peer review. We highlight the importance of approaching an illustrated review from a storytelling perspective, and encouraging authors to keep their audience in mind when picking a theme or characters. We provide storyboard considerations and simplify graphic design principles to develop an outline and line draft for the illustrated review. We list programs available to authors to demystify creating attractive and engaging scientific visuals. Finally, we provide information on choosing colors or fonts and where to find copyright-free icons, graphics, illustrations, and pictures. This review provides prospective authors with the knowledge, tools, and resources to create an effective illustrated review article. If there is difficulty with the links embedded within the document please download the full PDF.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it