Bibliographic record
Abstract
The University of Calgary's Libraries and Cultural Resources became a beta partner with Serials Solutions’ unified discovery service, Summon, in the spring of 2009. Since then it has worked to include metadata from numerous disparate systems in a single index to drive discovery in a Google-like environment. The University has examined how MARC and other metadata schemas are mapped into Summon with an eye to ensuring the maximum possible population of index fields representing facets in addition to adhering to the established standards for cross mapping metadata schemas and indexing. It has investigated existing standards and worked closely with the Summon team to create mappings that reflect how MARC and other metadata can ultimately be used in big indexes. Combined with the normalization or collapsing of metadata records representing the same resource into a single metadata-rich record, fully leveraging MARC and other metadata in big indexes should not only level the metadata playing field but make competition between records a non-issue.
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 | 0.066 | 0.218 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.032 | 0.094 |
| Open science | 0.007 | 0.046 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".