Current use and trends of Geospatial Collection Development Policies (GCDPs) in Map/GIS Libraries
Bibliographic record
Abstract
The rapid increase of publications both in print and digital form raises costs while academic libraries budgets are constantly decreasing. At the same time academic libraries cannot ignore the continuous spread of open geographical data on the web. The construction of policies consist a major and substantial function for any library in order to develop geospatial collections and provide added value services to its users. \nBased on this rationale, the purpose of the current research is to determine the availability of geospatial collection policies and identify their specific characteristics as they emerge through their published texts. \nThe population of these policy texts comes from the U.S.A., Canada, Australia and Europe, e.g. regions where the libraries have developed similar collections. In order to approach the topic of geospatial collection policies, two methodologies were used: a) research on libraries’ websites and b) content analysis. The sample of libraries that has been surveyed included 136 libraries with geospatial collections. In order to draw conclusions, it was necessary to determine the connection of the sample of libraries by participating in Map/GIS Libraries Associations such as ARL, MAGIRT, WAML, ANZMaps and MAGIC Group. \nFrom the sample of 136 libraries with collections and services regarding geographic information 53 (39%) policy documents were collected. The study of policy texts results their classification in six categories and relating to their extent they were divided into three types. After the examination of each text, the results were organized in tables and therefore eight major categories emerged. \nThe results of the research established a baseline information about the current use and trends of collection development policies in Map/GIS libraries and lead to some conclusions regarding the geospatial collection development environment.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.033 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".