Vancouver Statement on Collections as Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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, <em>Collections as Data: State of the Field and Future Directions, </em>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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.024 |
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 it