Mapping research networks supported by the National Geographic Society through spatial social networks
Bibliographic record
Abstract
Research is an interconnected global endeavor. Networks of research collaborations are often using Social Networks Analysis. Its variant Spatial Social Networks allowing explicit embedding of spatial information in the network. Variations in incorporating spatial information results in multiple conceptualizations of networks, enabling exploration of a variety of questions regarding collaborations. To elucidate this approach the National Geographic Society grants database (1890–2016) is utilized to create three different networks that embed spatial information in distinct ways. Each network highlights a different aspect of connectivity latent in the dataset and along with the spatial information, emphasizes international and regional trends of collaborations. The networks explicate the international nature of collaborative research by virtue of people collaborating explicitly, or by working in the same places. It also highlights the multidisciplinary nature of research in various countries, and how it can be useful to ideate about new projects. Additionally, the network approach highlights the dominance of global north in conducting fieldwork-based research across the world, mostly through collaborations. The abstraction afforded by social network models requires further deliberation on the way spatial relationships can be captured differently using the node-edge structure and how these alternate networks compare to traditional networks in GIScience.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.015 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".