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Record W2492273857

Response Bias in Voluntary Surveys: An Empirical Analysis of the Canadian Census

2016· preprint· en· W2492273857 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCarleton University's Institutional Repository (MacOdrum Library, Carleton University) · 2016
Typepreprint
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCensusTurnoverMisrepresentationDemographic economicsSurvey data collectionSurvey samplingAmerican Community SurveyGeographyHousehold incomeDemographyEconomicsStatisticsPolitical sciencePopulationSociology
DOInot available

Abstract

fetched live from OpenAlex

In 2011, the National Household Survey replaced the traditional Long Form Census in Canada. The questions in the National Household Survey were similar to the Long Form Census, but responding to this survey was no longer mandatory. This paper provides an empirical analysis of the information loss arising from the change to a voluntary response policy. Comparisons of the differences between the non-mandatory 2011 National Household Survey and the 1996, 2001, and 2006 mandatory Long Form Census are used to identify changes related to the response policy. Using two-sample Kolgomov-Smirnov tests, differences in income distributions are tested to find that high income earners are underrepresented in the voluntary survey. This finding is corroborated by comparisons of various inequality measures across these time periods. Differences in discrete variables are tested using differences in proportions and Pearsons chi-squared tests. Systematic misrepresentation of certain groups is found in the voluntary survey. The switch to a voluntary response policy in 2011 likely led to an over representation of women and married individuals.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0030.003
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.052
GPT teacher head0.267
Teacher spread0.215 · 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