Technologies of dispossession in the blue economy: Socio‐environmental impacts of seawater desalination in the Antofagasta Region of Chile
Bibliographic record
Abstract
Abstract Globally, ocean‐based economic development (aka the ‘the blue economy’) is increasing and continued growth is projected. The social and environmental impacts of the growth of the blue economy are a matter of some concern for local people who may experience few benefits and substantial negative impacts. One way that these impacts of the blue economy occur is through subtle or overt forms of dispossession that result from the growth of technologies and infrastructures at sea or on the land (e.g., offshore oil rigs, ships, ports, processing facilities, etc.). We focus on desalination, an understudied aspect of the blue economy that is growing exponentially. Proponents herald desalination as a sustainable solution to increase fresh water sources, while solving socio‐environmental issues left behind by the overuse of natural freshwater. However, far from being a social and environmental panacea, new political and economic interests are fused to sustain this technology with impacts for both the environment and local social systems. In this paper, we review the literature and develop a framework to understand mechanisms of dispossession via the blue economy and apply it to desalination projects in the Antofagasta Region of Chile. Drawing from a review of environmental impact assessments and declarations of environmental impacts, the research analyses communities' concerns about the impacts of desalination. Our analysis revealed (1) lack of meaningful inclusion in decision‐making; (2) physical displacement of communities or from areas required for livelihoods; (3) concerns about environmental effects, and related social impacts; and (4) few social or economic benefits to local people. The research concludes that desalination is a sector of the blue economy that is opening a new sphere of dispossession in the oceans.
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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 | 0.002 |
| 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 | 0.002 |
| 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".