Vancouver Statement on Collections as Data
Bibliographic record
Abstract
Arabic translation Spanish translation French translation Since the Santa Barbara Statement on Collections as Data (2017) was published, engagement with collections-as-data has grown internationally. Institutions large and small, individually and collectively, have invested in developing, providing access to, and supporting responsible computational use of collections as data. An updated statement was needed in light of increased community implementation of collections as data in context of an ever more complex data landscape. The Vancouver Statement suggests a set of principles for thinking through questions that collections-as-data work produces, as part of an expanding global, interprofessional, and interdisciplinary effort to empower memory, knowledge, and data stewards (e.g., practitioners and scholars) who aim to support responsible development and computational use of collections as data. This stewardship role only grows in importance as artificial intelligence applications, trained on vast amounts of data, including collections as data, impact our lives ever more pervasively. The Vancouver Statement is the product of diverse contributions from the participants of the working event, Collections as Data: State of the Field and Future Directions, held April 25-26, 2023 at Internet Archive Canada in addition to asynchronous community feedback. Professional translation of the Vancouver Statement was provided by Transolution. Special thanks go to Gimena del Rio Riande and Gaëlle Béquet for additional review of statement translations.
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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 | 0.016 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.013 | 0.022 |
| Insufficient payload (model declined to judge) | 0.073 | 0.082 |
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".