Bibliographic record
Abstract
Purpose This research project aims to provide a new visual representation of the Artefacts Canada digital collection, as well as a means for users to browse this content. Artefacts Canada Humanities is a database containing approximately 3.5 million records describing the different collections of Canadian museums. Design/methodology/approach A four‐step methodology was adopted for the development of the faceted taxonomy model. First, a best practice review consisting of an extensive analysis of existing terminology standards in museum communities and public web interfaces of large cultural organizations was performed. The second step of the methodology entailed a domain analysis; this involved extracting and comparing relevant concepts from terminological authoritative sources. The third step proceeded to term clustering and entity listing,which involved the breaking‐up of the taxonomy domains into potential facets. An incremental user testing was also realized in order to validate and refine the taxonomy components (facets, values, and relationships). Findings The project resulted in a bilingual and expandable vocabulary structure that will further be used to describe the Artefacts Canada database records. The new taxonomy simplifies the representation of complex content by grouping objects into similar facets to classify all records of the Artefacts Canada database. The user‐friendly bilingual taxonomy provides worldwide visitors with the means to better access Canadian virtual museum collections. Originality/value Few methodological tools are available for museums which wish to adopt a faceted approach in the development of their web sites. For practitioners, the methodology developed within this project is a direct contribution to support web site development of large cultural organizations.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".