The Canadian Linked Data Initiative: Charting a Path to a Linked Data Future
Bibliographic record
Abstract
This article is a preliminary report on the work of the Canadian Linked Data Initiative (CLDI), a collaboration between five of Canada’s largest research libraries, Library and Archives Canada, Bibliothèque et Archives nationales du Québec, and Canadiana.org. Although still in its nascent stage, participating institutions are working together to advance the technical services divisions of our libraries in the area of linked data. Project working groups are making progress in five main areas: grant funding, digital collections, education and training, legacy metadata enhancement, and in the evaluation and adaptation of Bibliographic Framework Transition Initiative tools. By working across geographic and institutional boundaries, the CLDI aims to chart a path to a new age of technical services, one based on the foundation of Linked Open Data.
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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 | 0.090 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.022 |
| Science and technology studies | 0.025 | 0.018 |
| Scholarly communication | 0.032 | 0.026 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".