Constructing patriotic networked publics: conservative YouTube influencers in Hong Kong
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
After the anti-extradition bill movement from summer 2019 until spring 2020, an upsurge in pro-government YouTube channels dramatically transformed the Hong Kong digital sphere. Using social media data and qualitative textual analysis, this commentary article examines the formation of patriotic networked publics by analyzing their participants, environments, and discursive practices in post-crisis Hong Kong. While the digital space in Hong Kong remains largely heterogeneous, the emergence of pro-government YouTube influencers has not only reshaped but also arguably reinforced the fragmented and polarized media landscape in Hong Kong. These influencers often utilize a mixture of nationalistic, conservative, and populist orientations, allowing them to demonstrate regime allegiance, advocate law and order, and frame themselves as the voice of the people through the strategic use of journalistic language. Parallel to the content providers of the alternative media outlets of the pro-democracy camp, these newer voices identified a niche and capitalized on the opportunity for fame. Their intervention unsettles the existing dynamics of the mediated public sphere, which has long been dominated by professional journalism and liberal discourse.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 it