Big Winners and Small Losers of Zero-rating
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An objective of network neutrality is to design regulations for the Internet and ensure that it remains a public, open platform where innovations can thrive. While there is broad agreement that preserving the content quality of service falls under the purview of net neutrality, the role of differential pricing, especially the practice of zero-rating , remains controversial. Zero-rating refers to the practice of providing free Internet access to some users under certain conditions, which usually concurs with differentiation among users or content providers. Even though some countries (India, Canada) have banned zero-rating, others have either taken no stance or explicitly allowed it (South Africa, Kenya, U.S.). In this article, we model zero-rating between Internet service providers and content providers (CPs) to better understand the conditions under which offering zero-rating is preferred, and who gains in utility. We develop a formulation in which providers’ incomes vary, from low-income startups to high-income incumbents, where their decisions to zero-rate are a variation of the traditional prisoner’s dilemma game. We find that if zero-rating is permitted, low-income CPs often lose utility, whereas high-income CPs often gain utility. We also study the competitiveness of the CP markets via the Herfindahl Index . Our findings suggest that in most cases the introduction of zero-rating reduces competitiveness.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it