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Record W6901497416 · doi:10.6068/dp14ba900010f88

Most Recent Data (2003). Statistics Canada. CANSIM: Business Performance and Ownership - Current Conditions | Country: Canada | Table: Survey of innovation, selected service industries, business unit success factors | Variable: Low importance, Encouraging experienced workers to transfer their knowledge to new or less experienced workers, Other support activities for mining, Non-innovative business units | Units: %, 2003. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-021.

2015· other· en· W6901497416 on OpenAlexaboutno aff

Bibliographic record

VenueData Planet · 2015
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic statisticsUnit (ring theory)Official statisticsCensusBusiness statisticsService (business)PublicationGross domestic productDescriptive statistics

Abstract

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Statistics Canada (2015). CANSIM: Business Performance and Ownership - Current Conditions | Country: Canada | Table: Survey of innovation, selected service industries, business unit success factors | Variable: Low importance, Encouraging experienced workers to transfer their knowledge to new or less experienced workers, Other support activities for mining, Non-innovative business units | Units: %, 2003. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. [Data-file]. Dataset-ID: 075-001-021. Dataset: Provides statistics on current trends in the Canadian economy, including Gross Domestic Product, employment, inflation, and trade. 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: Economic Cycles, Economic Conditions, Gross Domestic Product (GDP)

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 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)0.002
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Bibliometrics, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.389
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.141
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0050.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.083
GPT teacher head0.296
Teacher spread0.214 · 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; both teacher heads agree on what is shown here.

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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