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Top income shares in Canada: recent trends and policy implications

2012· article· en· W1490170635 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsMcMaster University
Fundersnot available
KeywordsIncome sharesEconomicsDemographic economicsSurgeFellPoliticsPromotion (chess)Development economicsPublic economicsGeographyPolitical scienceIncome distributionInequality

Abstract

fetched live from OpenAlex

According to Canadian taxfiler data, over the last thirty years there has been a surge in the income shares of the top 1%, top 0.1% and top 0.01% of income recipients, even with longitudinal smoothing by individual using three- or five-year moving averages. Top shares fell in 2008 and 2009, but only by a fraction of the overall surge. Alberta, British Columbia, and Ontario have much more pronounced surges than other provinces. Part of the Canadian surge is likely attributable to U.S. factors, but a comprehensive explanation remains elusive. Even so, I draw implications for policies that might achieve some support from across the political spectrum, including the elimination of tax preferences that favour those with high incomes, the promotion of shareholder democracy and, to maintain Canada's relatively high intergenerational mobility, continued wide accessibility to healthcare and education.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.115
GPT teacher head0.234
Teacher spread0.119 · 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