Exploring scientific collaborations in geographical information science (GIScience) : a study of its co-authorship networks
Bibliographic record
Abstract
Geographic Information Science (GIScience) as a discipline focuses on fundamental issues surrounding Geographic Information (Gl) and developments and applications of Geographical Information Technologies (GITechnologies). GIScience has accumulated a body of knowledge that can be easily exported and applied to other disciplines and assembled a wider multidisciplinary research community. \n \nCo-authorship networks are used to explore GIScience scientific collaborations during 1992-2002. Six different co-authorship networks were built from publication outlets comprising different sets of core and peripheral journals. The closer the periphery to the core, the more relevant the selected journals are to GIScience. Topological characteristics of all networks show similar networks despite the differences in sizes and the nature of the topics covered. However, networks with the peripheral journals closer to the core were more centralized around well-known scholars within the discipline. Furthermore, the network structures show a GIScience core linked to allied disciplines, especially to a highly clustered remote sensing research community. \n \nThe core co-authorship network was geo-referenced using authors’ affiliation information. The results show that geographical proximity, language and cultural preferences play important roles. Countries known for their strong publishing patterns in other sciences such as England, USA and Canada were alos identified within GIScience domain. A growth of international collaboration among Scandinavian and European Countries was revealed. Results also show that China, India and Brazil have been increasing their international participation within the GIScience research community.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".