Bibliographic record
Abstract
Abstract This article highlights the importance of an analysis of copyright law from a human development perspective. Drawing on Amartya Sen and Martha Nussbaum’s Capabilities Approach, it outlines why copyright scholarship and policymaking should address human capabilities. It also explores several vital questions that a human development approach to copyright raises, including questions about the distributional effects of copyright law. It examines Mary Sue fan fiction through the lens of the Capabilities Approach to illustrate how the approach differs from the standard utilitarian approach to copyright. Furthermore, it argues that several factors associated with a country’s level of development, particularly its social, economic, and institutional contexts, affect the relationship between copyright and human capabilities. Therefore, rather than making broad generalizations about whether or not copyright law is good or bad for human development, it concludes that aspects of copyright law can enhance human development in the presence of certain other factors (such as strong indigenous industries and institutions). Conversely, aspects of copyright law can have a significant negative impact on human capabilities in certain environments, such as a weak institutional environment, or a socio-economic environment that is fraught with inequality. To illustrate this point, the article examines the issue of piracy through the lens of the Capabilities Approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".