Celebrating 10 years of Government of Canada metadata standards
Bibliographic record
Abstract
As the Dublin Core Metadata Initiative celebrates its 15th anniversary, the Government of Canada (GC) celebrates its 10th year of making information easier to find. The Government of Canada officially adopted the Dublin Core as its core metadata standard for Web resource discovery in 2001. Soon the Government of Canada started to develop domain-specific metadata beyond Web and resource discovery to meet wider information needs. Supported by standards and other policy instruments, rapid metadata developments were made in the areas of records management, Web content management, e-learning, executive correspondence and geospatial data. The Government of Canada actively participated in the DC-Government Working Group, and organized its own event, the Canadian Metadata Forum in 2003 and 2005. More recently, the Government of Canada has adopted an enterprise information architecture (EIA) approach to metadata, within a larger information management strategy. The Government of Canada now has plans underway to develop other metadata domains, registries and repositories, its own namespace facility, and a vast awareness campaign to brand metadata as the “DNA of Government”.
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 | 0.027 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.025 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".