Putting Rooted Networks Into Practice
Bibliographic record
Abstract
Rooted networks provide a conceptual framework that embeds network thinking in nature-society geography in order to investigate socio-ecological relations, while emphasizing the place-specific materiality of these relations. This progress report examines how geographers have put the framework into scholarly practice. The conceptual approach has enabled researchers to: 1) articulate the territoriality and materiality of networks as assemblages, which may be simultaneously rooted and mobile; 2) discern diverse types of power that flow through network connections; and 3) conduct analyses that unearth multiply-situated knowledges within networks. Challenges emerge as we seek to integrate the approach more fully with disciplinary traditions, including organizing complex relationships into bounded scholarly formats; choosing which aspects of the network are most salient to analyze; and illustrating networks for effective communication. We describe the ways in which rooted networks can be used as a tool for action, as a pedagogical guide, and to strengthen collective capacity to imagine and negotiate alternative futures based on ‘seeing multiple.’ Finally, we call for geographers and other scholars, researchers and activists to build upon a rooted networks framework as a tool for design, analysis, understanding and communication in the search for more socially just and ecologically viable futures.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.017 | 0.031 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".