Channels Television on YouTube
Bibliographic record
Abstract
The migration of people in large numbers from Syria, Iraq, Afghanistan, Somalia, and South Sudan, to receiving countries such as South Korea, America, Canada, Russia, and Germany among others remains a challenge because of its attendant violence and conflicts. This study, using descriptive content analysis, examined the comments of Channels Television's YouTube channel commenters, as it relates to migration stories reported online. A total number of 30 YouTube videos on migration were selected based on their recency. Comments under the YouTube videos from January 2018 until April 2019, were examined using descriptive analysis to extract themes from these comments. The theories adopted for this study were the Framing and Priming theories. The analysis of public comments was to understand public discussions on migration and observe future implication of this discourse on inter-national relationships. Results revealed a possible future cultural divide among nations affected by migration if necessary actions are taken globally. The authors fear that such outcome could further promote disunity across nations and deprive individuals of their search for the greener pastures. The prevalent perceptions of the audience on the issue of migration which are advanced by these online comments can lure audience who read these comments but not involved in the discussions to believe and act it out.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.228 | 0.075 |
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".