Guerrilla geography: describing and defending place for a living (or the renaissance of 100–mile geographers)
Bibliographic record
Abstract
The abandonment of regional geography in the mid 20th century through pressures of globalization, urbanization and corporatization lost two generations of local knowledge and engagement. Studying place, finding the genius of loci, helping communities to articulate the uniqueness and relevance of place has been left to poets, activists and guerrilla geographers. The chapter looks at the role of guerrilla geography in the renaissance of place, community mapping and naming of place, and ultimately the protection and restoration of place through the word and illustrations of one practitioner from Canada’s rarest ecosystem—the Garry oak meadows overlooking the Salish Sea. Up until 1991, this drought‐adapted ecosystem, now the focus of research on ecosystem resilience in climate change, had no name, no map, no cultural identity beyond Little England, no recognition from academia and no protection. With the return to localism and demand for regional solutions, what is the role for young guerrilla geographers in their respective places across Canada? This discussion will chart a course of meaningful work as we pick up the lost stories of place and weave them with the new. It suggests ways for the academic community to support, educate and legitimize the next generation of guerrilla geographers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".