Bibliographic record
Abstract
In trying to promote the development of an open Internet, the U.S. Federal Communications Commission (FCC) has primarily tried to encourage network providers to be transparent about their traffic management practices and quality of service prioritization policies. Dominant network operators have successfully challenged this minimalist approach to addressing end-user concerns about the rise of a two-tiered Internet, motivating the FCC to engage in yet another public consultation process to assess its future approach to the problem. This article maps the debate using Natural Language Processing (NLP) tools that allow us to build a systematic picture of the positions of the regulator and groups of private interests trying to shape its decisions. A quantitative linguistic analysis of the content of formal written submissions to the FCC by parties with divergent views helps document how the conceptual model of the regulator evolved during the rulemaking process leading to the FCC February 2015 network neutrality Order. Despite the adoption of a broader substantive basis by the FCC under Title II of the Communications Act, the rule-of-reason approach to substantive interpretation in the Order limits the capacity of the new regulatory framework to protect and promote an open Internet. The evidence suggests the public consultation process is likely to serve as a tool for legitimizing status quo institutional arrangements that allow operators to engage in discriminatory traffic prioritization strategies.
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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 | 0.046 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.057 |
| Scholarly communication | 0.022 | 0.034 |
| Open science | 0.004 | 0.007 |
| 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".