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Record W2164011803 · doi:10.1177/0049124113506406

Prepaid Monetary Incentives—Predictors of Taking the Money and Completing the Survey

2013· article· en· W2164011803 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSociological Methods & Research · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSurvey Methodology and Nonresponse
Canadian institutionsImpactOntario Institute for Cancer ResearchUniversity of Waterloo
FundersNational Cancer Institute
KeywordsPrepayment of loanCashPaymentIncentiveSurvey data collectionSample (material)Demographic economicsSurvey methodologySurvey researchActuarial scienceBusinessEconomicsFinanceSocioeconomicsMedicineStatistics

Abstract

fetched live from OpenAlex

Prepaid monetary incentives are used to address declining response rates in random-digit-dial surveys. There is concern among researchers that some respondents will accept the prepayment but not complete the survey. There is little research to understand check cashing and survey completing behaviors among respondents who receive pre-payment. Data from the International Tobacco Control Four Country Study-a longitudinal survey of smokers in Canada, the US, the UK, and Australia, were used to examine the impact of prepayment (in the form of checks, approximately $10USD) on sample profile. Approximately 14% of respondents cashed their check, but did not complete the survey, while about 14% did not cash their checks, but completed the survey. Younger adults (Canada, US), those of minority status (US), and those who had been in the survey for only two waves or less (Canada, US) were more likely to cash their checks and not complete the survey.

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)0.347
metaresearch head score (Gemma)0.179
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.3470.179
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.009
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.678
GPT teacher head0.612
Teacher spread0.066 · 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