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Mobile Telephony as a Universal Service

2010· book-chapter· en· W4246799216 on OpenAlexaff
Ofir Turel, Alexander Serenko

Bibliographic record

VenueIGI Global eBooks · 2010
Typebook-chapter
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsLakehead University
Fundersnot available
KeywordsBusinessTelecommunicationsRevenueOrder (exchange)Customer baseService providerService (business)Quality of serviceTelephonyMobile telephonyMobile serviceMarketingQuality (philosophy)Computer scienceFinanceMobile radio

Abstract

fetched live from OpenAlex

The opening quote nicely conceptualizes one of the most difficult challenges managers and regulators in the telecommunications sector face. While such individuals are not, for the most part, concerned with world-safety, they do need to address similar diversity issues in order to be profitable and to provide true universal services (i.e., reasonably priced, high quality telecommunication services to everyone who wishes to use them). Similarly to John F. Kennedy, managers and regulators understand that one-service or set of regulations that fits all may not be a wise strategy. Rather, their offerings and regulatory mechanisms are always flexible, and they cater to a heterogonous subscriber market. While wireless service providers do try to cater to different market segments by offering a variety of service packages, regulators often employ a single set of regulations that serve the entire market. On the one hand, organizations offering mobile services to individuals attempt to segment the market to maximize various performance factors, such as usage airtime, revenues, and customer base. On the other hand, policies should be in place to avoid the discrimination of specific less profitable customer categories. In fact, in the 21st century, mobile telephony has become so critical for the well-being of millions of people that it is vital to ensure the fairness of mobile services delivery.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.937
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations5
Published2010
Admission routes1
Has abstractyes

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