Comparative Case Studies in Implementing Net Neutrality: A Critical Analysis of Zero Rating
Bibliographic record
Abstract
By Christopher T. Marsden. This article critically examines the relatively few examples of regulatory implementation of network neutrality enforcement at national level. It draws on co-regulatory and self-regulatory theories of implementation and capture, and interdisciplinary studies into the real-world effect of regulatory threats to traffic management practices (TMP). Most academic and policy literature on net neutrality regulation has focussed on legislative proposals and economic or technological principles, rather than specific examples of comparative national implementation. This is in part due to the relatively few case studies of effective implementation of legislation. The article presents the results of fieldwork in South America, North America and Europe over an extended period (2003-2015). The countries studied are: Brazil, India, Chile, Norway, Netherlands, Slovenia, Canada, United States, and those within the European Union. Empirical interviews were conducted in-field with regulators, government officials, ISPs, content providers, academic experts, NGOs and other stakeholders from Chile, Brazil, United States, India, Canada, United Kingdom, Netherlands, Slovenia, Norway. It also explores the opaque practices of co-regulatory forums where governments or regulators have decided on partial private rather than public diplomacy with ISPs, notably in the US, Norway and UK. The article notes the limited political and administrative commitment to effective regulation thus far, and draws on that critical analysis to propose reasons for failure to implement effective regulation. Finally, it compares results of implementations and proposes a framework for a regulatory toolkit. The specific issue considered are the tolerance of zero rating practices, notably as deployed by mobile ISPs.
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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 | 0.041 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| 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".