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Record W1976240950 · doi:10.1093/ijpor/edm023

Telephone Number Portability and the Prevalence of Cell Phone Numbers in Random Digit-Dialed Telephone Survey Samples

2007· article· en· W1976240950 on OpenAlexaboutno aff
Michael Link, Machell Town, Ali H. Mokdad

Bibliographic record

VenueInternational Journal of Public Opinion Research · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicSurvey Methodology and Nonresponse
Canadian institutionsnot available
Fundersnot available
KeywordsLandlineSoftware portabilityTelephone numberTelecommunicationsPhoneTelephone lineTelephonyBusinessComputer scienceAdvertisingComputer network

Abstract

fetched live from OpenAlex

Telephone number portability is a feature of circuit switch telecommunication networks that allows people to ‘port’ (i.e., permanently move or transfer) a telephone number from a landline to a wireless service, between two wireless services or, less frequently, from a wireless to a landline service. It was introduced in the United States as a means of improving competition among telecommunications companies and offering customers greater flexibility in telecommunications services (FCC, 2006 ). The problem for survey research is that telephone numbers thought to be landline numbers within a particular geographic area are sometimes later found to be associated with cell phones, which is problematic for more traditional random digit-dialed (RDD) telephone surveys that tend to exclude cell phone numbers. Number portability is occurring globally, with regulations varying across countries. Singapore was the first to introduce number portability in 1997. Number portability is also provided in the European Union, as well as in countries such as Australia, Brazil, South Korea, Argentina, Colombia, and Taiwan. Canada, India, Japan, and several other countries have plans in place to offer number portability in the near future (Rembert, 2006 ).

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)0.270
metaresearch head score (Gemma)0.091
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2700.091
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.350
GPT teacher head0.528
Teacher spread0.178 · 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; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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
Published2007
Admission routes1
Has abstractyes

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