Interrogating sustainable development and public utility : a case study of large-scale mining in Colombia
Bibliographic record
Abstract
In this paper, I examine the case of Angosturas, a large-scale gold mining project by the Canadian-based company GreyStar in the sensitive high wetland páramo of Santurbán in the northeast of the Colombian Andes. Angosturas is the closest large-scale gold mining project to the phase of extraction in the country, and constitutes a referent for other ongoing cases. In 2011, the Ministry of the Environment, Housing, and Territorial Development (hereafter: Ministry of the Environment) denied the environmental license to the company to start extraction operations. Despite this government’s recent ruling, GreyStar (which renamed itself ‘Eco Oro’ after the 2011 decision) and other mining companies have continued their quest to gain permission to begin extraction in the area. I explore why these continued attempts to change government policy regarding extraction licensing is possible within the existing juridical framework. In particular, I ask how Sustainable Development allows for the classification of large-scale mining as a public utility activity. This paper is a criticism of Sustainable Development and the limitations it places on hearing certain kinds of languages and discourses that resist the commodification of nature. The case study allows me to address a gap in the existing literature, and outline the distinctive situation of non-legally recognized ethnic minorities (e.g. peasant farmers, small-scale miners, and the urban population).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".