Understanding the net neutrality debate: Listening to stakeholders
Bibliographic record
Abstract
The Internet is increasingly seen as integral to economic progress and prosperity. Yet how the Internet will be managed as it grows and diversifies remains a hotly contested topic, as the debate on net neutrality demonstrates. Whether the Internet is neutral or not has serious implications for Internet service providers (ISPs), businesses operating online, governments, and civil society. With these stakeholders and varying interests at play, the debate about net neutrality is often characterized in terms of polar positions, and the discussion has seemed intransigent and ongoing with an uncertain end point. To increase understanding about the debate, this paper combines a review of the literature on net neutrality with evidence from interviews with four individuals, each representing the viewpoint of a major stakeholder group in Canada. Analysis of the similarities and differences among key stakeholder positions shows that in fact the positions are more complex and considerably more nuanced than typically depicted. By focussing on components of the issues, and staying away from the politics of contesting net neutrality, progress in the debate can be made. While this paper gives attention to the Canadian context in particular, the findings echo those of international organizations, and adds to the global conversation on the future of the Internet.
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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 | 0.040 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.039 | 0.053 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".