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Record W2890728275 · doi:10.3386/w20160

Weak Versus Strong Net Neutrality

2014· preprint· en· W2890728275 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNational Bureau of Economic Research · 2014
Typepreprint
Languageen
FieldSocial Sciences
TopicMedia Influence and Politics
Canadian institutionsUniversity of Toronto
FundersMicrosoft Research
KeywordsNet (polyhedron)NeutralityNet neutralityMathematicsComputer scienceThe InternetPhilosophyWorld Wide WebEpistemologyGeometry

Abstract

fetched live from OpenAlex

This paper provides a framework to classify and evaluate the impact of net neutrality regulations on the allocation of consumer attention and the distribution of surplus between consumers, ISPs and content providers. While the model provided largely nests other contributions in the literature, here the focus is on including direct payments from consumers to content providers. With this additional price it is demonstrated that the type of net neutrality regulation (i.e., weak versus strong net neutrality) matters for such regulations to have real effects. In addition, we provide support for the notion that strong net neutrality may stimulate content provider investment while the model concludes that there is unlikely to be any negative impact from such regulation on ISP investment. Counter to many claims, it is argued here that ISP competition may not be a substitute for net neutrality regulation in bringing about these effects

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.839
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.491
GPT teacher head0.578
Teacher spread0.087 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it