Capturing and Articulating Visual Media as Scholarship
Bibliographic record
Abstract
Recent advancements in social media and educational technologies have facilitated dissemination of information in visual formats to broad audiences. When academic and creative visual media work is of high quality and accessible via various platforms (eg, in journals, on websites), it can meet the definition of scholarship.However, visual scholarship is sometimes viewed as an afterthought. For example, even venues that include visual abstracts with manuscripts may encourage authors to create visual abstracts post-submission to promote the article rather than as an independent scholarly pursuit. Typically, annual performance reviews, accreditation requirements, and promotion and tenure processes do not recognize or reward visual media as scholarly activity, despite the considerable technical and pedagogical skills required for its creation.The updated Accreditation Council for Graduate Medical Education (ACGME) Common Program Requirements define scholarship as including discovery, application, and teaching.1 This position builds on antecedent work by Boyer and Glassick.2,3The Declaration on Research Assessment (DORA) has encouraged scholars to equitably acknowledge each form of scholarship by using specific quality metrics, rather than relying on surrogate measures such as impact factor.4 DORA explores how we can value various forms of visual scholarship as meaningful contributions to the medical and medical education communities. For example, infographics and tweetorials can be examined by dissemination measures using journal-based or platform-based metrics such as Altmetric or Twitter analytics, respectively.5
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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 | 0.016 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.023 | 0.016 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.027 | 0.032 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".