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Record W6976951561 · doi:10.6068/dp14ba84425c721

Trend 1980 - 1994. Statistics Canada. CANSIM: Retail and Wholesale - Retail Sales by Type of Product | Country: Canada | Table: Direct selling, by method of sale and commodity | Variable: Toys, games, crafts, cards, Sales by other methods | Units: $CAD x 1,000, 1980-1994. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-176.

2015· other· en· W6976951561 on OpenAlexaboutno aff

Bibliographic record

VenueData Planet · 2015
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCommodityProduct (mathematics)Economic statisticsCensusRetail salesOfficial statisticsSummary statisticsDistribution (mathematics)Retail trade

Abstract

Statistics Canada (2015). CANSIM: Retail and Wholesale - Retail Sales by Type of Product | Country: Canada | Table: Direct selling, by method of sale and commodity | Variable: Toys, games, crafts, cards, Sales by other methods | Units: $CAD x 1,000, 1980-1994. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. [Data-file]. Dataset-ID: 075-001-176. Dataset: Provides statistics on retail sales in Canada by type of product (commodity). Estimates of the distribution of the sales in retail outlets are broken down for more than 100 commodity groups. The retail industry is primarily engaged in selling consumer goods and related services to the general public. CANSIM is Statistics Canada's key socioeconomic database. The datasets included here provide statistics on the Canadian population, and the nation’s resources, economy, society, and culture. In addition to conducting a Census every five years, approximately 350 active surveys are conducted on virtually all aspects of Canadian life. Statistics are provided for the nation as a whole, provinces, and other subnational geographies where available. Category: Industry, Business, and Commerce Source: Statistics Canada Established as Canada's central statistical office by the Statistics Act of 1985, Statistics Canada is required to "collect, compile, analyse, abstract and publish statistical information relating to the commercial, industrial, financial, social, economic and general activities and conditions of the people of Canada." Its main objectives are to provide statistical information and analysis about Canada’s economic and social structure and to promote sound statistical standards and practices. http://www.statcan.gc.ca/ Subject: Retail Sales, Consumer Products, Retail Trade

Stored with the screening record, where it is evidence for the labels above.

How this classification was reachedexpand

The three-model screen

all 5,600 screened works →

All three models called this out of scope.

stratum: about_only · design weight: 3321.24 (the sample is stratified; any rate computed without the weight is wrong)
Claude Opus 4.8OUT
genre: other
about Canada: no
confidence: high

Statistics Canada retail sales data table; economic statistics unrelated to research.

GPT-5.6 (high)OUT
genre: other
about Canada: no
confidence: high

The dataset reports Canadian retail sales rather than research activity.

Grok 4.5OUT
genre: other
about Canada: no
confidence: high

Retail sales statistical data product; no relation to research as object.

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)0.002
metaresearch head score (Gemma)0.015
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.088
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0100.045
Science and technology studies0.0030.001
Scholarly communication0.0050.003
Open science0.0050.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0880.062

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 head0.042
GPT teacher head0.295
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
Published2015
Admission routes1
Has abstractyes

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