We are all (not) Anonymous: Individual- and country-level correlates of support for and opposition to hacktivism
Bibliographic record
Abstract
Hacktivists oftentimes challenge or subvert existing power relations or structures and attempt to promote reform. How the public perceives occurrences of hacktivism can influence the direction and impact of operations, including their potential success. Public support can encompass person power, computational ability, resources and solidarity, among other things. This study examines socio-legal contexts in which an individual is embedded and personal perceptions as predictors for support for hacktivism. Using representative survey data from 23 countries ( n = 23,140), the study finds that more effective civic participation mechanisms and more positive views toward alternative actors and hacktivists’ utilitarianism motives were associated with heightened support. In contrast, greater trust in legal and state authorities promoted opposition. Effective justice was not associated with more support but was with less opposition for hacktivism. Implications for campaigns, social movements, and desistance of activity are discussed.
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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 | 0.000 | 0.000 |
| 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".