Indirect Outreach in a GIS Environment: Reflections on a Map Libraryâs Approach to Promoting GIS Services to Non-GIS Users
Bibliographic record
Abstract
Promoting non-traditional library resources to the academic community can be a challenging and sometimes ineffective process if not coordinated and planned thoroughly. The University of Waterloo Map Library (UML), specializing in cartographic and geospatial data resources, diligently visits dozens of classrooms yearly, promoting its resources and services. The nature of the content is often intimidating to the students however, because Geographical Information System (GIS) technology is unfamiliar to them. In September 2007, the Map Library instigated a new indirect outreach approach of connecting with students and introducing them to geospatial information literacy concepts. Instead of leaving students confused about the technology and the library’s vast amount of resources, the new approach encourages students to learn at their own pace and leaves them responsible for seeking more information when they need it. This approach has resulted in establishing relationships with many more students, and thus increasing the number of GIS users in the library and on campus.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.015 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".