Hostile terrain: on the spatial and affective conditions for revolution
Bibliographic record
Abstract
In 1966, Ernesto ‘Che’ Guevara arrived in south-east Bolivia assuming that the region’s forested mountains and the poverty of the peasantry constituted ‘favourable terrain’ to start a revolution. Instead, he encountered a hostile terrain that led to the defeat of his guerrilla force and to his death. In this article, I offer a spatial and affective analysis of Guevara’s conceptualizations of ‘favourable’ and ‘unfavourable’ terrain, of his gendered experience of a ‘hostile terrain’ in Bolivia, and of how these ideas and his emphasis on revolutionary determination were subsequently debated and reformulated by guerrilla fighters and radical movements in Latin America. Drawing from an analysis of the interface between terrain, place and territory in rebellions, I show how the dichotomy between favourable and unfavourable terrain misses that spatially attuned insurrections can potentially weaponize any type of terrain, but also that they always confront a ‘hostile terrain’, understood as the social and territorial conditions that hinder their spatial proliferation. This means conceptualizing revolutions as spatial and affective processes through which determined multitudes overcome this hostility by attuning to place, empowering their strategies through engagements with different types of terrain, and expanding rebel territories. I conclude by discussing why these questions are relevant today to radical politics amid the climate crisis.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".