An Action Research Approach helps Develop GIS Programs in Humanities and Social Sciences
Bibliographic record
Abstract
A Review of: Kong, N., Fosmire, M., & Branch, B. D. (2017). Developing library GIS services for humanities and social science: An action research approach. College & Research Libraries, 78(4), 413-427. http://dx.doi.org/10.5860/crl.78.4.413 Abstract Objective – To develop and improve on geographic information systems (GIS) services for humanities and social sciences using an action research model. Design – Case study. Setting – A public research university serving an annual enrollment of over 41,500 students in the Midwestern United States. Subjects – Faculty members and students in the humanities and social sciences that expressed interest in GIS services. Methods – An action research approach was used which included data collection, analysis, service design, and observation. Interviews with 8 individuals and groups were conducted including 4 faculty members, 3 graduate students, and one research group of faculty and graduate students. Data from interviews and other data including emails and notes from previous GIS meetings were analyzed and coded into thematic areas. This analysis was used to develop an action plan for the library, then the results of the activity were assessed. Main Results – The interviews revealed three thematic areas for library GIS service: research, learning, and outreach. The action plan developed by the authors resulted in increased engagement including active participation in an annual GIS day, attendance at workshops, course-integrated GIS sessions, around 40 consultations on GIS subjects over a two-year period, and increased hits on the Library’s GIS page. Surveys from pre- and post-tests in the workshops increased participants’ spatial awareness skills. Conclusion – Using an action research approach, the authors were able to identify needs and develop a successful model of GIS service for the humanities and social sciences.
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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 | 0.133 | 0.077 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".