The Research Data Centre Programme in Canada: a holistic approach to evidence‐based research to inform public policy
Bibliographic record
Abstract
As part of a response to the challenges that confront Canadian policy research, a joint task force assembled by the Social Sciences and Humanities Research Council (SSHRC) and Statistics Canada proposed the creation of a series of Research Data Centres (RDCs). The network of RDCs was formally launched in December 2000 with the opening of the centre at McMaster University in Hamilton, Ontario. The RDCs are located throughout the country, so researchers are not obliged to travel to Ottawa to access Statistics Canada data. At the same time, the centres are administered in accordance with all the confidentiality rules required under the Statistics Act . The Research Data Centres meet, in a single location, both the need to facilitate access to detailed micro‐data for crucial social research and the need to protect the confidentiality and security of Canadians' information.
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 | 0.352 | 0.291 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.027 | 0.051 |
| Science and technology studies | 0.019 | 0.026 |
| Scholarly communication | 0.029 | 0.015 |
| Open science | 0.015 | 0.030 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".