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Record W2465076690

Modeling and estimation methods for household size in the presence of nonignorable nonresponse applied to the Norwegian consumer expenditure survey

2005· preprint· en· W2465076690 on OpenAlexaboutno aff
Liv Belsby, Jan F. Bjørnstad, Li‐Chun Zhang

Bibliographic record

VenueBIBSYS Brage (BIBSYS (Norway)) · 2005
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsnot available
Fundersnot available
KeywordsNorwegianConsumer Expenditure SurveyPermissionOfficial statisticsGovernment (linguistics)EstimationZhàngReproductionEconometricsStatisticsEconomicsPublic economicsPolitical scienceLawMathematicsChina
DOInot available

Abstract

fetched live from OpenAlex

Survey Methodology ia an open access journal published by Statistics Canada. This reproduction ia a copy of an official work that is published by the Government of Canada. The reproduction has not been produced in affiliation with, or with endorsement of the Government of Canada. Permission to reproduce the article is obtained from Li-Chun Zhang, Statistics Norway and from Statistics Canada.

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)0.054
metaresearch head score (Gemma)0.121
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.057
Threshold uncertainty score0.287

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0540.121
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0030.005
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0050.002
Research integrity0.0030.004
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 head0.161
GPT teacher head0.306
Teacher spread0.145 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations3
Published2005
Admission routes1
Has abstractyes

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