Bibliographic record
Abstract
This thesis explores a puzzling phenomenon that defies common intellectual property (IP) wisdom: creative industries that produce high volumes of creativity, without the use of IP law. It addresses two key questions: What are the factors that sustain high levels of creative production and effectual social order in these creative industries? and what does this tell us about IP law and theory? It proposes an explanatory framework that accounts for successful creative production and social order, how obstacles are overcome, and how different actors contribute to creative production, thus developing new taxonomies and models for IP and ‘order without law’ scholarship. This thesis breaks new methodological ground by systematically synthesising and analysing multiple studies of such creative industries in conjunction, using legal, sociological, and economics scholarship, as well as narratives in popular culture such as social media and books. Based on a set of original questions, it locates different variables that affect creative production and social order. From these, it refines a set of five common features that explain the success of these creative industries: community – a distinct, organised group with shared goals, ethos, or interests; effective information networks; clear norms with effective compliance mechanisms and means for dispute resolution; law or legal institutions play an important role; and, that there are institutions for giving credit and attribution. Looking at these common features, this thesis also exposes that external players, such as suppliers, reviewers, bloggers, consumers, and fans, have a key role, alongside creators, in sustaining creative production and social order. It demonstrates the extent and magnitude of external players’ impact, by illustrating their roles in all of the five common features. For example, they monitor and enforce norms, disseminate information, and provide essential infrastructure for social interaction. Reflecting on this explanatory framework – the set of common features sustained by creators and various external players – I highlight several important implications: suggesting new taxonomies for self-governance by adding flexibility to current definitions of certain social institutions; a synergetic approach to the relationship between law and social norms; adding certain exceptions to IP law; and giving wider consideration to attribution rights.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".