Bibliographic record
Abstract
The 2011 Canadian Research Data Summit was held at the Ottawa Convention Centre on September 14. About the Summit On September 14-15, 2011, The 2011 Canadian Research Data Summit brings together 100-150 senior researchers, high level policy makers, university administrators, and members of the private sector. Together, participants will work on formulating a shared strategy for addressing the challenges and opportunities for maximizing the benefits of our collective investment in research data in Canada. The Summit will act as a catalyst for the development of a made-inCanada approach for maximizing the availability and use of research data. About the Research Data Strategy Working Group The Research Data Strategy Working Group is a collaborative effort launched in 2008 to address the challenges and issues surrounding the access and preservation of data arising from Canadian research. This multi-disciplinary group of universities, institutes, libraries, operators of research infrastructure, granting agencies, governments, and individual researchers are united through a shared recognition of the pressing need to deal with Canadian data stewardship issues. Together, they are focussing on the necessary actions, next steps and leadership roles that researchers and institutions can take to ensure Canada’s research data are accessible and usable for current and future generations of researchers
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 | 0.043 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.018 | 0.003 |
| Scholarly communication | 0.021 | 0.004 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.046 | 0.026 |
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".