Telephone Number Portability and the Prevalence of Cell Phone Numbers in Random Digit-Dialed Telephone Survey Samples
Bibliographic record
Abstract
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 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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".