Dissemination and the Digital: The Creation of an Academic Book Trailer
Bibliographic record
Abstract
Academics who concentrate on the study of Islam live in challenging times. The proliferation of “popular” sources of news and information evokes both significant concern as well as tremendous possibility. This is true across the academy, not only in our own field. In a recent issue of theJournal of the American Medical Association, a team of medical scientists analyzed 153 videos about vaccination and immunization on YouTube. What they found was very disturbing. A staggering number of YouTube videos portrayed vaccinations in a negative light, and about half contained messages completely contradicting established medical science. Furthermore, the research team found that videos with negative portrayals of vaccinations were highly provocative and powerful, and received more views and better ratings by YouTube users than those videos that portray vaccinations in a positive light. The study concludes that this situation is extremely dangerous and that public health officials must consider how to effectively communicate their scientifically founded viewpoints through internet video portals.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".