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Record W3016216813 · doi:10.3233/sji-190601

Past, present and future of Canadian statistics

2020· article· en· W3016216813 on OpenAlexaffabout
Jean‐Guy Prévost

Bibliographic record

VenueStatistical Journal of the IAOS · 2020
Typearticle
Languageen
FieldMathematics
TopicCensus and Population Estimation
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsStatisticsEconometricsEconomicsMathematics

Abstract

fetched live from OpenAlex

This paper presents an overall view of the evolution of Canada’s central statistical office since the advent of modern Canada in 1867. It is a history characterized by four periods of significant bureaucratic and scientific developments, each followed by phases of stasis, flux, uncertainty and setbacks. Development can be linked to the conjunction of major turns in government’s policy needs, the adoption of technical or managerial innovations and the presence of a strong bureaucratic leadership, while setbacks occur when one of these factors is found wanting.

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)0.017
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.964

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.020
Science and technology studies0.0070.008
Scholarly communication0.0120.005
Open science0.0030.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0130.002

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 head0.050
GPT teacher head0.314
Teacher spread0.264 · 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 designNot applicable
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

Citations2
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueStatistical Journal of the IAOSSame topicCensus and Population EstimationFrench-language works237,207