Instagram as a virtual art display for medical students
Bibliographic record
Abstract
Implication Statement We require our medical students to create art as part of a core course. Projects have historically been displayed at our health sciences library. During a rapid adjustment to virtual teaching at the onset of the COVID-19 pandemic, using Instagram to hold a virtual art show was a quickly implemented alternative. With student consent, course directors posted different artwork every weekday for eight weeks to a course account. By capitalizing on the visual strengths and extensive reach of the Instagram platform, we promoted our medical students' talents both locally and nationally. We plan to use Instagram and in-person displays in the future. Énoncé des implications de la recherche Dans un de leurs cours du tronc commun, nos étudiants en médecine sont amenés à faire de l'art. Avant la pandémie de la COVID-19, leurs projets étaient exposés à la bibliothèque des sciences de la santé. La solution de rechange trouvée dans le cadre d'une adaptation rapide à l'enseignement virtuel au début de la pandémie a été d'utiliser Instagram pour exposer virtuellement les œuvres. Avec le consentement des étudiants, les responsables de cours ont publié des œuvres différentes tous les jours pendant huit semaines sur un compte Instagram créé pour le cours. Tirant parti des atouts visuels et de la vaste portée de la plateforme, nous avons pu promouvoir les talents de nos étudiants en médecine tant au niveau local qu'au niveau national. À l'avenir, nous comptons combiner les expositions physiques et Instagram.
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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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.235 | 0.051 |
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".