Piracy by Approval: Social Norms, Deterrence, and Copyright Compliance in China Compared to the United States
Bibliographic record
Abstract
This study seeks to comparatively understand copyright piracy amongst Chinese and American students. It tested the influences of deterrence, social norms, and perceived duty to obey the law on the likelihood that the 216 participants would engage in digital piracy in two hypothetical digital offending scenarios. Half of the participants were subjected to an “enforcement campaign” condition that indicated the enforcement crackdown on digital piracy. The other half did not receive information about enforcement of digital copyright infringement. Results indicate that regardless of explicit campaign enforcement, Chinese students’ inclination to engage in digital piracy hinges chiefly on the perceived behavior and approval of others. This stands in contrast to the US students for whom the enforcement campaign changed influences on their behavior. Both social norms and perceived deterrence affected decision-making during the explicit crackdown, whereas both social norms and perceived duty to obey the law affected decision-making when there is no explicit crackdown. The study provides broader implications both for enforcement policy and for comparative compliance theory.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".