Building interoperable Canadian architecture collections: initial metadata assessment
Bibliographic record
Abstract
Purpose The purpose of this research is to assess the current descriptions of architecture collections housed at the McGill University Library in preparation for building an interoperable metadata and search interface for Canadian architecture collections. Design/methodology/approach The names and frequencies of tables and fields of 11 architecture databases were analyzed and summarized into the most commonly used groups. In addition, typologies of buildings by purpose of construction were presented as subject headings. Findings Current metadata schemes are diverse and heterogeneous across the 11 databases. Research limitations/implications This study is at the pilot stage and is limited to Canadian architecture collections at McGill University. The observations provide insights into metadata normalization that can be used as a basis for building architecture collections or image collections. Originality/value This is the first metadata assessment of architecture collections for the purpose of building a single uniform access.
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 | 0.030 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.033 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".