Constructing patriotic networked publics: conservative YouTube influencers in Hong Kong
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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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".