Understanding Digital Scholarship Needs to Support the Evolving Nature of Academic Research
Bibliographic record
Abstract
Libraries are transforming spaces and services to better support the nature of 21st century research. For example, many academic libraries are developing digital scholarship centres or labs. This lightning talk will provide an overview of two consultation processes employed at the University of Calgary to gather feedback from scholars on how the library could support new modes of research. The University of Calgary undertook two consultation processes: the first was a focused workshop conducted in 2015 with scholars from three multidisciplinary research clusters: arctic studies, smart cities, and visual analytics. The second was a series of semi-structured interviews conducted in 2016 with individual scholars from a range of disciplines in the arts and humanities. The workshop participants highlighted common research support needs including data and data repositories,digitization, expertise, space, skills training, and funding for collaborations. The interview participants highlighted similar needs, with space, access to interdisciplinary collaborators, and training/consultation being the most commonly identified research support needs. This talk will be of interest to other academic libraries wishing to reshape library spaces and services to support the research needs of their local community. Key outcomes include: Understand trends in how libraries are supporting the evolving nature of academic resource. Learn about how one library gathered feedback on research support needs from scholars. Understand how existing library resources and expertise can be coordinated and/or reallocated to support new modes of research.
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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 | 0.094 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.025 | 0.036 |
| Scholarly communication | 0.056 | 0.063 |
| Open science | 0.005 | 0.042 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".