Establishing an international computational network for librarians and archivists
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.
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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 | 0.051 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.027 | 0.037 |
| Open science | 0.008 | 0.047 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.053 | 0.025 |
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".