Avoiding the Pitfalls of Net Uniformity: Zero Rating and Nondiscrimination
Bibliographic record
Abstract
The current debate over network neutrality has not fully appreciated how service differentiation can benefit consumers and promote Internet adoption. On the demand-side, service differentiation addresses the primary obstacle to adoption, which is the lack of perceived need for Internet service, and reflects the growing heterogeneity of consumer demand. On the supply-side, monopolistic competition has long underscored how product differentiation can create stable equilibria with multiple providers – notwithstanding the presence of unexhausted economies of scale – by allowing competitors to target subsegments of the overall market that place a higher value on particular services. Conversely, prohibiting service differentiation would restrict competition to price and network size, which are factors that favor the largest players. These dynamics are well illustrated by global enforcement patterns with respect to a practice known as “zero rating,” which permits subscribers to access certain content without having that traffic count against their data caps. Of the six countries that have brought enforcement actions against zero rating, only India has categorically banned the practice. The other five countries (the United States, Chile, Canada, Slovenia, and the Netherlands) have adopted a more nuanced approach. A case-by-case approach is consistent with the empirical literature on vertical integration and restraints and the well-established principles for determining when to impose per se illegality and when to apply the “rule of reason.” The U.S. Supreme Court’s antitrust jurisprudence also helps identify factors that militate against liability, such as the lack of market power, nonexclusivity, and nonproprietary services.
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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 | 0.031 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.012 |
| 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".