Bibliographic record
Abstract
Traditional education continuously faces the challenge of encouraging students to care about the subject material. Edutainment (educational entertainment) has been one of the attempted solutions to this issue. There are several problems with existing edutainment; first, not all of it is created equal - topics in student and mental health are drastically underexplored compared to traditional lessons in science. Additionally, much of edutainment is still burdened with using traditional academic techniques as its core to engage the audience, with visuals and entertainment being used as an enhancement to the experience. However, entertainment is inherently based on tried and tested practices that engage the audience [Speer et al. 2009; Zak 2014]. This talk focuses on flipping this model. With edutainment, entertainment and user experience practices should be the core - with the details of the academic content being secondary to the overall experience. In this way, the subject material is not discussed in detail, and the primary goal is to excite the audience. My approach has been with science cartooning - writing comics and stories that communicate topics in health. Hired by the University of British Columbia (UBC) Digital Emergency Medicine team, I helped create an interactive graphic novel for the BC curriculum called The Adventures of Patoo [Mortazavi et al. 2018], which covers topics in physical and mental health for students in grades 4-7. The positive student and teacher feedback from the school pilots of The Adventures of Patoo is a good indication that edutainment does not have to carry much educational content. It can just be a teaser trailer.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.404 | 0.078 |
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".