The Role of Next Generation Libraries in Enhancing Multidisciplinary Research
Bibliographic record
Abstract
How well are research libraries positioned to meet the needs of today’s multidisciplinary research? What are the common needs across disciplines conducting such research? These are the questions the University of Calgary Libraries and Cultural Resources sought to answer through focused discussions among researchers extending over three days in the fall of 2015. With funding from The Andrew W. Mellon Foundation, we hosted workshops with researchers from three multidisciplinary research clusters of strategic priority to the University of Calgary – Arctic Studies, Smart Cities and Visual Analytics. External facilitators managed the various sessions and three disciplinary experts from other Canadian universities (Toronto, Carleton and Queens) contributed in broadening the scope of the inquiry. Library staff and representatives of the University’s Research Services Office acted principally as observers, but contributed as needed in identify existing research infrastructure capacities. In this project briefing, we will discuss the planning and conducting of these workshops, report on the common research support needs and themes, examine the implications for 21st century libraries in planning services and technologies and explore their role in the development of research platforms.
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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 | 0.197 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.038 | 0.023 |
| Scholarly communication | 0.078 | 0.061 |
| Open science | 0.006 | 0.072 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".