If a picture already tells a thousand words, how many more do we need to add?
Bibliographic record
Abstract
We review the unique opportunities that video production provides for teaching and learning, with a focus on storytelling using sounds, images, and yes, even (occasionally) words. Subject Matter Expertise is rooted in the written word and the public presentation, but contemporary tools and new methods of distribution require educators and experts to learn a new kind of media literacy. Innovate or perish. Communicate or disappear. We take the audience on a journey from written word to motion picture, using examples of our past video productions ‐ from the high production value 4K UBC Neuroanatomy Series to shorter, cost effective, more focused lessons on human anatomy shot with a smartphone. We show that video production is not only a necessary piece of the new learning ecosystem, but one that you can engage with directly using tools you might already have in your pocket. Engaging and thought provoking, this hands‐on session will create a small army of fledgling filmmakers. Support or Funding Information None
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".