Bibliographic record
Abstract
Over the past several decades, the direct intersection between university libraries and academic research has noticeably diminished. In recent years, however, new relationships between libraries and researchers have begun to evolve based on shared interest in emerging technologies, research techniques, and the perceived need, particularly in the social sciences and humanities, for robust digital platforms and services. This paper reviews outcomes of a multi-year, $1M study funded by The Andrew W. Mellon Foundation conducted at the University of Calgary, Academic Research and University Libraries: Creating a New Model for Collaboration. This project seeks to identify, through evidence-based inquiry, what constellation of services will be necessary to support today’s multidisciplinary research. It also reflects the broad recognition, as seen in funder and university research priorities, that “grand challenges” of this century require true cross-disciplinary inquiry. How are research libraries responding to these changes? This study began in 2015 and has evolved over several phases. The initial phase involved more than 50 faculty, from 15 disciplines, in facilitated discussions to define what they needed from a 21st-century library to support their multidisciplinary research. The discussions included external experts, and library staff participation as observers but not contributors to ensure the deliberations were not overly influenced by library-centric perspectives. Building on scholars’ expressed needs, the Library undertook a second phase of inquiry through competitive sub-grants, in which faculty proposed research projects and worked with library staff to define what the needed services, expertise, and infrastructure would be. In two successive competitions, 12 faculty projects have been approved with approximately $400,000 in internal grants disbursed. The process includes external reviews by expert panels. Through these studies, essential elements of the new research platform have emerged: • analytics and visualization • data curation and sharing • digitization to support content analysis • metadata services • dissemination • rights management • virtual and augmented reality • design and modelling for digital – 3D creation • web programming • collaborative spaces Other key findings include: • the importance of direct collaboration between library staff and scholars, and between library staff in different areas, in shaping services • the essential role of a library research project facilitator • the new synergies between digital content and analytical tools • how special collections can be exploited in new ways, e.g., text mining • a need for lab-like spaces to support evolving research methodologies. Faculty who may seldom have been seen in the library are now using these lab environments and incorporating students into the experience. Senior research administrators involved in the study have been learning about, and promoting, these new library capacities. Ultimately, the findings emerging from this and related studies will inform conversations about research libraries’ future roles, and their continuing relevance in the modern research ecosystem. Presentation video at https://www.youtube.com/watch?v=jUldbTN64Nk.
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 | 0.093 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.024 |
| Science and technology studies | 0.024 | 0.035 |
| Scholarly communication | 0.096 | 0.086 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".