Dissemination and the Digital: The Creation of an Academic Book Trailer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 the Journal 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.
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.
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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it