Sustainable Development as a Wicked Problem: The Case of the Brazilian Amazon Region
Bibliographic record
Abstract
The global importance of the Amazon rainforest is abundantly evident. It is the world’s largest tropical rainforest, home to incredible biodiversity, and arguably an essential part of the earth’s already fragile climate system. Exploiting the Amazon beyond planetary boundaries, in other words, beyond a threshold the forest cannot regenerate itself from, may have catastrophic, global impacts. Conversely, Brazil is an industrialized yet developing country far from realizing its potential to become an economic superpower. To this day, millions of Brazilians still live below the poverty line. Hence, Brazil cannot afford to ignore the economic potential of the Amazon’s vast resources. The issue becomes how to use those resources effectively and efficiently for economic development. The key is perhaps a sustainable development approach. Considering the diversity of internal (i.e., Brazilian) and external (i.e., foreign) stakeholders, there is no agreement of what sustainable development would mean when it comes to the Brazilian Amazon Region (BAR). The challenge is the fact that environmental and social problems are not just complex problems. According to Brown et al (2010), they are very hard to properly define. Buchanan (1992) concluded these types of problems do not fit within any specific subject matter. In this research, we propose the sustainable development of the BAR as a wicked problem (Rittel and Webber, 1973). The main purpose of this study is to conduct an analysis of stakeholders to confirm this hypothesis. This study employs systems thinking, specifically Peter Checkland’s (1989) Soft System Methodology as the conceptual foundation of the analysis of evidence from the field. Preliminary findings are summarized, and conclusions with conceptual and practical considerations are provided. Limitations and opportunities for future studies are also included. Keywords: sustainable development, wicked problems, Brazilian Amazon, system thinking, soft system methodology.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".