Balancing openness and prioritization in a two-tier Internet
Bibliographic record
Abstract
The open Internet is plagued by congestion that restricts the development of sophisticated Internet-based services as was predicted in early work on priority pricing.Broadband and edge providers have proposed a two-tier Internet with fee-based prioritization of traffic in a fast-lane Internet that coexists with the open Internet to overcome these problems.This requires a restriction of Internet openness, also known as network neutrality, in the fast-lane Internet.Opponents of a two-tier Internet believe it would hinder innovation, motivate underinvestment in Internet infrastructure and consequently reduce the quality of service (QoS) of the open Internet.The challenge is for policy to balance a fee-based fast-lane for priority traffic and safeguard the viability of the open Internet.In our model, edge providers choose output levels and which Internet to use, a broadband provider chooses investment in Internet capacity and pricing for prioritizing traffic in the fast-lane, and a policy-maker chooses a mechanism for balancing openness and prioritization in a two-tier Internet.We find that edge providers with greater bandwidth requirements per unit of output convert to the fast-lane and that the fast-lane can drive innovation from edge providers with high bandwidth requirements.The broadband provider chooses fixed fee pricing for the fast-lane but has no incentive to increase investment in Internet capacity as long as the open Internet is not monetized.So long as there are no investments in Internet capacity, all edge providers of the open Internet and their end users are worse off with a two-tier Internet.To maintain the QoS of the open Internet and to increase social welfare, a two-tier Internet has to be coupled with a policy mechanism whereby a portion of broadband provider profit is invested in Internet capacity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".