Native History and Nation Building on Personal Online Platform: Implications in Hong Kong Context
Bibliographic record
Abstract
Nationalism in the era of social media is more complex and presents new opportunities and challenges in different levels and contexts. Therefore, the paper hopes to contribute to understanding the roles of social media in identity presentation and formation in a transition society. Writing on Facebook is a civil practice. Thus, it chooses a typical and clear-cut Facebook fan page “Hong Kong National History” run by a nationalist and followed by over 5700 fans as a case study. Posts of the fan page are collected from 1 April to 31 December in 2017, and it analyzes the contents and forms of posts with content analysis. Then, the self-made digital publication “Hong Kong People’s History of the Thousand Years” attached to the fan page is analyzed with narrative analysis. Through the personal systematic discourses, this paper presents a special mode of user-generated content online and a civic Hong Kong story.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".