The nature of metaphors in cultural geography and environmental history
Bibliographic record
Abstract
This chapter reviews metaphors in environmental history and cultural geography which is intended to point the way towards some new metaphorical terrain, indicated by the work of Bruno Latour and Donna Haraway. In cultural geography, the appeal of new landscape metaphors is bound up with a critique of Sauer's methodological discussions of landscape. Cultural geographers appropriated metaphors of cultural production to turn attention towards the social construction of meaning. One of the earliest and most influential applications of the Geertz's text metaphor in cultural geography is David Ley's evocative analysis of the landscapes of inner Vancouver. The metaphors of Latour and Haraway, like the metaphors of environmental history and cultural geography, enable some critical projects while they proscribe others. The chapter discusses the different ways in which their metaphors enframe nature and enable us to think about it simultaneously as an embodied material actor and as a socially constructed object.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".