The Geospatial Data User Experience: Examining Approaches and Challenges in Canadian Academic Libraries
Bibliographic record
Abstract
This study aims to provide detailed information on the geospatial data management practices of Canadian academic libraries as they relate to providing a quality user experience. In the summer of 2024, the researchers conducted six semi-structured interviews with library staff members at six Canadian institutions (Carleton University, Dalhousie University, University of Alberta, University of British Columbia, University of Manitoba, and the University of Toronto) about their geospatial data management practices and priorities. This research outlines the systems, strategies, and approaches taken at these institutions related to the tasks of curating, managing, distributing, or supporting the use of geospatial data. Analysis of the interview transcripts revealed common challenges among the institutions, such as accounting for a variety of expertise levels among users, as well as insufficient data discovery infrastructure, funding, and staff capacity. The data also suggests that the demand for geospatial data is growing, that staff providing these services are currently under-resourced, and that there may be significant advantages to consortium models for data access and management.
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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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.035 | 0.014 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".