Internet freedom and copyright maximalism: Contradictory hypocrisy or complementary policies?
Bibliographic record
Abstract
U.S. advocacy for increased international intellectual property protection and a free and open Internet has been criticized as being inconsistent at best and hypocritical at worst. Placing U.S. copyright and Internet policy in a historical context and using Susan Strange's concepts of structural power and knowledge structures, we argue that copyright and Internet policies cannot be analyzed in isolation, but are intimately and inextricably linked forms of knowledge regulation. All knowledge regulation policies involve balancing access and restriction. Our analysis suggests that the current U.S. policy of Internet freedom and strong copyright protection represents a particular, historically situated strategy designed to exert structural power in the global information economy: Free flow of information creates markets by exposure to intellectual properties, while copyright secures economic benefit to copyright holders from the flow. We argue that a full and honest debate over issues of information access requires acknowledgment of contemporary and conflicting values, with the realization that different societies and interests will weigh access and dissemination differently. Recognizing as legitimate and incorporating these different perspectives into the global governance structures of the Internet comprise the key challenge facing those who favor truly democratic global Internet governance.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.069 |
| Scholarly communication | 0.019 | 0.029 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".