A missing link? Network analysis as an empirical approach for critical physical geography
Bibliographic record
Abstract
Abstract Critical physical geography (CPG) calls for integrative research on material landscapes and the socio‐political dynamics of scientific knowledge production. Network analysis, a rich tradition of tools and approaches for analyzing relational information, has seen little use in the CPG literature to date. This represents a fruitful opportunity, as many of CPG's core interests—knowledge politics, histories of scientific concepts, and ecosocial relations—can be effectively analyzed using network techniques. In this article, I argue for adapting network approaches to CPG. First, I provide an overview of various network concepts, approaches, and their origins. I then discuss bibliometric network techniques for “science mapping” including co‐word, co‐authorship, and citation analyses. Next, I describe discourse network analysis, a recent mixed‐method approach from political science. Finally, I discuss overlaps with emerging approaches from qualitative and visual network analysis. In each section, I provide existing and hypothetical examples, as well as software and visualization techniques, that demonstrate how network approaches could add new insights to CPG and related scholarship. Linking CPG with the diverse traditions of network analysis has the potential to produce new empirical understandings and bring the field into conversation with a growing body of research that spans the social sciences, natural sciences, and humanities.
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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 | 0.025 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".