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Receipt of Travel Survey Advance Letter and Its Impact on Reported Trips and Number of Phone Calls for Survey Completion in Telephone Surveys

2016· article· en· W2516476291 on OpenAlexaboutno aff
Daniel A. Badoe, Angela Biney

Bibliographic record

VenueJournal of Urban Planning and Development · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSurvey Methodology and Nonresponse
Canadian institutionsnot available
Fundersnot available
KeywordsTRIPS architectureRespondentReceiptPhoneSurvey data collectionTelephone surveyTravel surveySurvey methodologyGeographyTravel behaviorBusinessTransport engineeringAdvertisingMedicineStatisticsEngineeringPolitical scienceMathematics

Abstract

fetched live from OpenAlex

Research is conducted into the effect the receipt of a travel survey advance letter, sent as part of a household travel survey, has on the person trips reported by a household’s survey respondent for each household member and on the number of phone calls made to a household to successfully complete the survey using two data sets collected in household travel surveys conducted in the Greater Toronto Area in 2001 and 2006. The results of the statistical analysis of the data led to the conclusion that not receiving the survey advance letter resulted in survey respondents significantly underreporting the trips made by their respective household members. The underreporting varied with trip purpose, with home-based mandatory trips not being underreported while home-based discretionary and non-home-based trips were significantly underreported. Additionally, households not receiving the survey advance letter required significantly more phone calls to complete the survey compared to households that received the letter.

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 imitation

Not 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.

metaresearch head score (Codex)0.050
metaresearch head score (Gemma)0.276
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.950
Threshold uncertainty score0.264

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.276
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.216
GPT teacher head0.435
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainMethods
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

Citations0
Published2016
Admission routes1
Has abstractyes

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