Metadata and controlled vocabularies in the government of Canada: a situational analysis
Bibliographic record
Abstract
This paper describes the Government of Canada's standards and recent activities to create and manage metadata and controlled vocabularies. The Government of Canada (GoC) has been working actively for several years to enhance access to its published information through the use of metadata. In recognition of the value of controlled vocabularies in managing electronic information, the GoC has adopted standards for metadata and controlled vocabularies. Various initiatives have been proceeding to create and adopt controlled vocabularies for use with Dublin Core and other metadata schemas. Work is proceeding simultaneously on several fronts: establishing governance and developing tools to create and adapt controlled vocabularies, extensibility and interoperability frameworks, development of metadata registries and repositories, and creation and mapping of taxonomies. Canadian government departments and agencies have engaged in these metadata initiatives to support the fundamental priority of transforming services. The challenge is to allow the initiatives to mature and develop while ensuring they are co-ordinated.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.033 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".