Relational tactics and trust in high-risk activism: Anonymity, preexisting ties, and bonding in Hong Kong’s 2019–2020 protest
Bibliographic record
Abstract
Trust, the belief that others will fulfill their expected obligations, is of vital importance to social movements. While most research focuses on how trust facilitates protests, this article examines how high-risk activism participants deploy various relational tactics to reduce uncertainties and risks. With an in-depth case study of Hong Kong’s protest movement in 2019–2020, this article finds anonymity, preexisting ties, and bonding are the three common responses. Across various interpersonal settings, participants strategize personal trust or minimize their risk exposure, finding ways to collaborate with strangers or acquaintances. Trust is more than a preexisting resource; it can also be created during movement mobilization. Trust enables protest participation, and it also aids in logistics provisioning, as well as sheltering and aftercare of activists. Finally, government repression has worked in part because it aims to undermine the trust networks built up during the movement.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".