Building the collections of tomorrow
Bibliographic record
Abstract
Position Statements for the international forum: Collections as Data: State of the field and future directions, a working event held April 25-26 in Vancouver, Canada. \n \nCultural heritage curation and data curation are information specializations that are, and should be, increasingly intersecting, especially with the rapid growth of digital cultural heritage resources and tools created by ambitious digitization programs and the rise of complex, computation-driven research in the digital humanities and adjacent fields. Curators have immense power to shape collections – libraries, archives, and museums acquire, describe, interpret, digitize, preserve, and facilitate access to key government and business records, cultural heritage materials, and innumerable unique resources. Curation practice is moving beyond the FAIR6 principles into the CARE principles, which recognizes and empowers the humans and communities often at the center of data collection. This shared area of investment by curators, both of cultural heritage and research data, is a space in which we can support and learn from one another.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.090 | 0.021 |
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".