What Potential for YouTube as a Policy Deliberation Tool? Commenter Reactions to Videos About the Keystone XL Oil Pipeline
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
Social network sites have been proposed to influence the way interest groups and citizens interact on various policy topics. User reaction to information received on YouTube can be partially observed by examining comments provided as part of the interface. Using content analysis, this article explores the way YouTube users interact with information provided by media, interest groups, and other groups through user comments. While a large number of comments are found to be ad hominem or off‐topic, in general, user comments on the controversial Canada–U.S. Keystone XL oil pipeline cover collectively the main topic areas found in the December 2, 2013 U.S. Congressional Research Service study of the issues. User comments also reflect a preferential network structure where the existence of a comment makes it more likely that someone will reply to commenters rather than the video itself. The article concludes with some comments on the potential of YouTube as a policy deliberation tool .
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 | 0.003 |
| 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 | 0.001 | 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