Geographic Information Services in the Undergraduate College: Organizational Models and Alternatives
Bibliographic record
Abstract
Although GIS is well established at most research universities, it has only recently been integrated into the instructional programs of many undergraduate colleges. This article explores three organizational models for implementing GIS in the undergraduate environment: the departmental model (GIS based in a single academic department), the non-departmental model (GIS based in another agency, such as the library or the computer centre), and the “no centre” model (in which there is no identifiable centre for GIS despite its use in teaching and research). Survey results from 55 liberal arts colleges in the United States reveal that institutions with GIS centres (departmental or otherwise) tend to have enhanced instructional capabilities, better institutional support, and greater capacity to handle large-scale demand for GIS. Non-departmental GIS centres are associated with several additional advantages: the availability of GIS courses in a wide range of academic disciplines, increased support for faculty just beginning to learn GIS, and higher potential demand for GIS resources and services. These findings may indicate that non-departmental GIS centres encourage the diffusion of GIS across multiple departments, or that non-departmental centres are most likely to emerge at colleges where GIS is already well established across a range of disciplines.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".