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Consumption Inequality in Canada, 1997 to 2009

2014· dataset· en· W6926345450 on OpenAlexaboutno aff

Bibliographic record

VenueFederated Research Data Repository · 2014
Typedataset
Languageen
FieldMedicine
TopicMicrobial Natural Products and Biosynthesis
Canadian institutionsnot available
Fundersnot available
KeywordsGini coefficientInequalityConsumption (sociology)Imputation (statistics)Economic inequalityRentingIncome inequality metrics

Abstract

fetched live from OpenAlex

We assess the evolution of consumption inequality in Canada over the years 1997 to 2009. We correct the imputation of shelter consumption for homeowners to allow for unobserved differences in housing quality correlated with selection into rental tenure, and we account for measurement error in this imputation. Using the Surveys of Household Spending 1997-2009, we find that household-level consumption inequality measured by the Gini coefficient increased from 0.251 to 0.275 over 1997 to 2006, and then declined to 0.264 by 2009. The Gini coefficient for individual level inequality similarly followed a hump-shaped pattern: it increased from 0.199 in 1997 to 0.216 in 2006, and then fell to 0.207 in 2009. In contrast, the Gini coefficient for household level income inequality followed a similar hump-shaped pattern, but the post-2006 decline was large enough to entirely wipe out pre-2006 increase. We also explore a possible correction for tail non-response bias in inequality measurement, and find that the increase in measured consumption inequality is robust to this correction. This dataset was originally deposited in the Simon Fraser University institutional repository.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.036
Threshold uncertainty score0.264

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.017
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.129
GPT teacher head0.383
Teacher spread0.254 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2014
Admission routes1
Has abstractyes

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