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Record W6931695398 · doi:10.5683/sp3/mnccu7

Canada Survey of Giving, Volunteering and Participating, 2007 [Canada]: Giving File

2023· dataset· en· W6931695398 on OpenAlexaboutno aff

Bibliographic record

VenueBorealis · 2023
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlanarian Biology and Electrostimulation
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Agency (philosophy)General partnershipGeneral Social SurveyHealth statisticsSurvey data collectionOfficial statistics

Abstract

fetched live from OpenAlex

The Canada Survey of Giving, Volunteering and Participating 2007 is the latest iteration of a series of surveys that began with the National Survey of Giving, Volunteering and participating. It was conducted by Statistics Canada in 1997 as a supplement to the Labour Force Survey, and was repeated in the fall of 2000. In 2001, the federal government provided funding to establish a permanent survey program on charitable giving, volunteering and participating within Statistics Canada. The survey itself was renamed the Canada Survey of Giving, Volunteering and Participating (CSGVP). The CSGVP was developed through a partnership of federal government departments and voluntary sector organizations. These include Canadian Heritage, Health Canada, Human Resources and Social Development Canada, Imagine Canada, the Public Health Agency of Canada, Statistics Canada and Volunteer Canada. There are two data files for the 2007 Canada Survey of Giving, Volunteering and Participating (CSGVP): the Main answer file (MAIN.TXT), and the giving file (GS.TXT). The 2007 CSGVP was conducted by Statistics Canada in the provinces and territories from September 10th to December 8th 2007.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.151
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.013
GPT teacher head0.238
Teacher spread0.225 · 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 teacher head, 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
Published2023
Admission routes1
Has abstractyes

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