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Record W1984055542 · doi:10.3138/cpp.2012-073

Opting or Not Opting to Share Income Tax Information with the Census: Does It Affect Research Findings?

2014· article· en· W1984055542 on OpenAlexaffvenueabout
Pierre Brochu, Louis‐Philippe Morin, Jean‐Michel Billette

Bibliographic record

VenueCanadian Public Policy · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsStatistics CanadaUniversity of Ottawa
Fundersnot available
KeywordsAffect (linguistics)CensusEconomicsWageDistribution (mathematics)Income distributionDemographic economicsEconomic inequalityIncome taxInequalityActuarial scienceOpting outGross incomeLabour economicsPublic economicsEconometricsState income taxTax reformSociologyDemographyMathematics

Abstract

fetched live from OpenAlex

This paper examines the implication of the decision to give 2006 Census respondents the option of letting Statistics Canada access their income tax files rather than answering income-related questions directly. We find that giving respondents the option to share their income tax files (or not) adds a confounding factor when it comes to measuring family-income inequality, particularly for the bottom tail of the distribution. The consent decision does not, however, materially affect the estimation of standard wage equations.

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)0.251
metaresearch head score (Gemma)0.583
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.979
Threshold uncertainty score0.924

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2510.583
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.010
Science and technology studies0.0050.014
Scholarly communication0.0070.006
Open science0.0040.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.056
GPT teacher head0.346
Teacher spread0.290 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainReproducibility
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

Citations7
Published2014
Admission routes3
Has abstractyes

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