Canada Survey of Giving, Volunteering and Participating, 2007 [Canada]: Giving File
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.020 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.017 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".