Intellectual Property Norms in Online Communities: How User-Organized Intellectual Property Regulation Supports Innovation
Bibliographic record
Abstract
In many online communities, users reveal innovative and potentially valuable intellectual property (IP) under conditions that entail the risk of theft and imitation. When there is rivalry and formal IP law is not effective, this could lead to underinvestment or withholding of IP, unless user-organized norms compensate for these shortcomings. This study is the first to explore the characteristics and functioning of such a norms-based IP system in the setting of anonymous, large-scale, and loose-knit online communities. To do so, we use data on the Threadless crowdsourcing community obtained through netnography, a survey, and a field experiment. On this basis, we identify an integrated system of well-established norms that regulate the use of IP within this community. We analyze the system’s characteristics and functioning, and we find that the “legal certainty” it provides is conducive to cooperation, cumulative effects, and innovation. We generalize our findings from the case by developing propositions aimed to spark further research. These propositions focus on similarities and differences between norms-based IP systems in online and off-line settings, and the conditions that determine the existence of norms-based IP systems as well as their form and effectiveness in online communities. In this way, we contribute to the literatures on norms-based IP systems and online communities and offer advice for the management of crowdsourcing communities.
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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 | 0.009 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".