Establishing an international computational network for librarians and archivists
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
Research and experimentation are underway in libraries, archives, and research institutions on various digital strategies, including computational methods and tools, to manage "Collections as Data." This involves new ways for librarians and archivists to manage, preserve, and provide access to their digital collections. A major component in this ongoing process is the education and training needed by information professionals to function effectively in the 21st century. Accessible and transferable infrastructure is a key requirement in creating a network of collaboration for information professionals to fully realize the full potential of managing "Collections as Data." Elements needed include: 1. Open source research and educational platforms to remove barriers to access to curation tools and resources. These are needed to deliver and share computational educational programs. 2. Creation of a Cloud-based student-learning environment. 3. Development of Open Source software architectures that use computational infrastructure. 4. Exploration of new pedagogies for educating librarians and archivists in computational methods and tools. 5. Establishment of a community of practice for developing collaborative projects, and liaising with the wider international iSchool community and practitioners in the field. Our "Blue Sky" proposal seeks to explore a number of these challenges (infrastructure, computation, collaboration, learning) that stimulate the iSchool research community and have the potential to jumpstart international collaborative networks. The goal is to establish an international computational network for supporting librarians and archivists, akin to the existing Sloan Foundation funded "Data Curation Network", which seeks to model a cross-institutional staffing approach for curating research data in digital repositories.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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