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Record W2809631190 · doi:10.29007/ctcq

Local Patterns of National Household Survey Non-Response in Canadian Cities

2018· paratext· en· W2809631190 on OpenAlexaffabout
Scott Bell, Kelsey M. Bates, Kyle Snarr, Jessica Alegria

Bibliographic record

VenueEasyChair preprint · 2018
Typeparatext
Languageen
FieldMathematics
TopicCensus and Population Estimation
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCensusGeographyUnit (ring theory)American Community SurveyGovernment (linguistics)Ethnic groupData collectionSurvey data collectionOfficial statisticsRegional scienceDemographyPolitical scienceStatisticsSociologyPopulationPsychologySocial science

Abstract

fetched live from OpenAlex

Statistics Canada and the Canadian government invoked a dramatic change in the collection of detailed demographic and other data for the Census year 2011. Despite reverting in 2016 to the traditional “long form” census format, the National Household Survey (NHS) of 2011 represents an important and meaningful opportunity for study. Furthermore, with a 10-year gap between instances of the more reliable “long form” survey format, users of detailed census data products face challenges if interested in demographic, economic, social, and other changes that happened between 2006 and 2016 or trends in such data over a period that includes the 2011 NHS. Here we examine patterns of non-response, using the variable Global Non-Response (GNR) in several Canadian cities using dissemination areas (DA) as the unit of analysis. We will also show patterns of similarity and dissimilarity with GNR and other NHS variables (social, demographic, ethnic, housing, etc.).

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)0.006
metaresearch head score (Gemma)0.027
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 score0.994
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.017
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0030.003
Research integrity0.0000.001
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 head0.096
GPT teacher head0.344
Teacher spread0.248 · 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
Published2018
Admission routes2
Has abstractyes

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