Predicting Moral Disengagement From The Harms Associated With Digital Music Piracy: An Exploratory, Integrative Test Of Digital Drift And The Criminal Interaction Order
Bibliographic record
Abstract
This exploratory paper estimates a predictive model for moral disengagement from harms associated with digital music piracy. Our approach is founded upon Goldsmith and Brewer’s (2015) proposed digital criminal interaction order. This framework suggests that electronic resources (e.g. social media; message boards; digital texts; etc.) may act as proxy for conventional social interaction in learning deviant ideologies and developing neutralizing strategies. To the best of our knowledge, this theory has not yet been empirically tested. To this end we developed an integrated research tool and administered it to a non-random sample of 625 people. The test includes measures for technological competency; capacity to mask personal identity online; affinity modeling deviant behaviors encountered online; positive affect for engaging in digital deviance; and moral disengagement. A multiple linear regression of the standardized variables indicated that digital capacity for identity protection, affinity for modeling, and positive affect for digital deviance significantly predicted moral disengagement from the harms associated with digital music piracy (F = 94.011, p < .05, adj. R2 = .319). Further implications from these findings are discussed in relation to music piracy specifically, and digital deviance generally.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".