Planning for the <i>Buen Vivir</i> : socialism, decentralisation and urbanisation in rural Ecuador
Bibliographic record
Abstract
Guided by the Buen Vivir (good living) developmental philosophy, Alianza PAIS, Ecuador’s leftist party in power since 2007, aims to ‘urbanise’ rural areas in order to decentralise economic activity and the population away from the main cities. Through a case study of a poor rural region of Ecuador, our research provides insights into how planning, modernisation goals and Buen Vivir are intertwined and explores the challenges of operationalising state development and planning ideals. This paper is structured into four sections. First, we situate Ecuador’s urbanisation agenda in the context of broader urbancentric poverty reduction strategies, socialist planning ideologies and Latin American decentralisation. Second, we introduce Ecuador’s national urbanisation strategy through the government document, El Plan Nacional para el Buen Vivir (PNBV). Third, we critically examine efforts to urbanise a rural canton, focusing particularly on the divergences between the spirit of the PNBV and local urbanisation efforts, and local residents’ responses to urbanisation schemes. Finally, we suggest that despite the state’s official rhetoric about the inherent equity of decentralisation and the transformative potential of Buen Vivir, rural urbanisation practices demonstrate a perpetuation of neo-extractivist priorities and a continuation of state control over rural areas and their resources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".