Bibliographic record
Abstract
The Government of Canada is bringing together related information and services across organizational boundaries into groupings or clusters that make sense to clients. The strategy begins with an Electronic Cluster Blueprint- a starting set of subject clusters, each representing a complete set of information andservices on a particular subject. The information and services referenced atthe 35 cluster sites provide thousands of links to federal departments andagencies, provinces and a multitude of private and not-forprofitorganizations. The actual content resides at organizational web sites, withcluster sites providing a subject-oriented approach for clients to find information regardless of host organization. Each cluster site is a portalwhich provides context information derived from metadata. The metadata helpsclients find information they are looking for (resource discovery), and metadata helps cluster managers administer and maintain content at the portal (manage information). Some clusters provide substantial context through a rich metadata set; other clusters provide a minimum set of metadata and encourage the client to go directly to the information source. Some metadata elements are cluster specific such as geographic coverage or industrial sector. Cluster managers require a flexible and dynamic metadata set. However, organizational web sites are the content providers and the authoritative source of content and the primary source of metadata. Therefore they must follow a common metadata standard and content rules. A central metadata repository is envisioned. All content providers will contribute metadata through a common process which will be used by the various clusters. The presentation will describe the role metadata plays in the portalenvironment and how Dublin Core meets these metadata needs.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.011 |
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".