The Canadian Linked Data Summit: Developing Canada's Linked Data Future through Cooperative Alliances
Bibliographic record
Abstract
From October 24 to 26, 2016, the Canadian Linked Data Initiative (CLDI) hosted the Canadian Linked Data Summit in Montreal, Quebec with the goal to increase awareness and nurture collaboration for linked data production in Canada. The Summit was inspired by CLDI’s investment in developing and sustaining a cooperative plan for Canadian linked data development for libraries, archives, museums, and other cultural institutions across the country. CLDI, comprising of Canada’s five top research libraries, the University of Toronto, McGill University, Université de Montréal, University of Alberta, and the University of British Columbia, and partners at Library and Archives Canada, Bibliothèque et Archives nationales du Québec, and Canadiana.org, organized the CLDI Summit to allow library staff specializing in cataloguing and technology from institutions across Canada to become better equipped for opening our library metadata to the global Web through the production of linked data. Gathering together linked data experts from North America and Europe, librarians from academic, government and special libraries, as well as graduate students from Canadian Library and Information Science schools, the CLDI Summit provided a forum for recognizing the importance of linked data for libraries, sharing expertise and resources, and working collaboratively between units and institutions across the country. Consisting of presentations and panel discussions, hands-on workshops, and a stakeholders planning meeting, the 3-day CLDI Summit helped to ignite and sustain real strategies for how to move forward with linked data knowledge and production in Canada through leadership, collaboration and communication.
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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 | 0.076 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.036 | 0.010 |
| Scholarly communication | 0.035 | 0.013 |
| Open science | 0.010 | 0.034 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".