<scp>I</scp>ntervention: Critical physical geography
Bibliographic record
Abstract
Abstract A recent opinion piece rekindled debate as to whether geography's current interdisciplinary make‐up is a historical relic or an actual and potential source of intellectual vitality. Taking the latter position, we argue here for the benefits of sustained integration of physical and critical human geography. For reasons both political and pragmatic, we term this area of intermingled research and practice critical physical geography (CPG). CPG combines critical attention to power relations with deep knowledge of biophysical science or technology in the service of social and environmental transformation. We argue that whether practiced by individuals or teams, CPG research can improve the intellectual quality and expand the political relevance of both physical and critical human geography because it is increasingly impractical to separate analysis of natural and social systems: socio‐biophysical landscapes are as much the product of unequal power relations, histories of colonialism, and racial and gender disparities as they are of hydrology, ecology, and climate change. Here, we review existing CPG work; discuss the primary benefits of critically engaged integrative research, teaching, and practice; and offer our collective thoughts on how to make CPG work.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".