Uploader Motivations and Consumer Dynamics in the One-Click File Hosting Ecosystem
Bibliographic record
Abstract
Internet piracy is a significant ongoing problem for content producers and rights holders. Estimates of the cost of copyright infringement to the film and music industries range in the tens to hundreds of billions of dollars every year. The vast majority of this illegal content is shared using three key technologies: peer-to-peer (P2P) protocols such as BitTorrent, illegal file streaming, and one-click file hosting services (OCHs). The current dominant analogy for file- sharing, promoted by copyright holders and industry lobby groups, is one of 'copyright theft'; with content uploaders predominantly depicted as opportunists motivated by financial gain. Recently, academics from various disciplines have begun to question this narrative, proposing alternative models for understanding piracy based on the concept of the social or 'altruistic' sharer. In this paper, two OCH indexes were studied for insights into uploader dynamics. Results suggest that traditional understandings of Internet piracy are significantly limited in their ability to explain a number of aspects of the current OCH ecosystem. A significant number of uploaders are found to be behaving in ways that do not fit the traditional economic narrative; large numbers of users are making negligible money, and aggregate figures show a significant amount of uploaders are failing to take actions to appropriately maximise their hypothetical earnings.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".