Crack and release: a study of pirate culture, community, and folklore
Bibliographic record
Abstract
This dissertation is an ethnographic study of the culture and folklore of a digital media pirate community at the late Kickass Torrents (KAT) on the eve of its shutdown. Seized in 2016 by the U.S. Department of Homeland Security, KAT had been home to a community that was rich in folklore, folklife, and illegal file-sharing. Based on ethnographic fieldwork at KAT, I argue that piracy is a vernacular tradition in which digital media are materially and symbolically appropriated, transformed, and reproduced as folk variants to create in a virtual pirate commons. Digital media piracy is highly contentious, yet, as a cultural practice, it is more nuanced than commonly depicted in discourse. Understanding it requires contextualizing it within a set of contested histories. Although legally considered a tort of copyright infringement, media piracy has for centuries been rhetorically linked with the crime of maritime piracy. This association is used strategically by anti-piracy campaigns but also by pirates themselves as they draw on folklore and outlaw folk heroes as they discursively create their identities. Similarly, media piracy is intertwined with the histories of and sociocultural anxieties surrounding the ideological origins of copyright and industrial mass production. Piracy calls into question issues of ownership and authenticity, values which lie at the heart of modernity. Far more than illegal downloading, piracy becomes a symbolic threat to the social order. With digital technologies increasingly integrated into everyday life, media piracy is an issue that will only continue grow in significance. Although there is considerable literature on it, very little of it is ethnographic. I address this through interviews, questionnaires, and participant observation at KAT and I show how a folkloristic approach is best suited for interpreting the vernacular ways in which pirates negotiate illegality and a contested discourse by creating community and expressive culture.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".