How to build resilient communities? The Chilean Environmental Observatory as an interdisciplinary research case driven by systemic design to empower through information
Bibliographic record
Abstract
Faced with the climate crisis scenario, it is urgent to take action for local, sustainable and resilient community development, such as the collaboration of multiple actors from civil society and the private and public sectors. In that regard, public environmental information on environmental management and the performance of the mining industry is key to reducing socio-environmental conflicts and supporting decision-making, especially for citizens who are on constant alert in the face of the climate crisis. Recognising this problem, a multidisciplinary research team from the Pontifical Catholic University of Chile developed the Environmental Observatory (EO), an environmental management information system pilot focused on mining projects in two regions of Chile. The purpose of the EO is to improve access to information for resilient community action to respond to environmental crisis contexts and avoid the generation of environmental conflicts due to lack of information, lack of transparency in environmental management processes and distrust associated with the control of the mining industry. Today, access to this kind of information in Chile has weaknesses linked to the institutional dispersion of data, asymmetries of access to information by different actors and the scarce consideration of the capacities, needs and expectations of each type of actor involved in a territory’s environmental management. The wicked problems approach, visual thinking, sense-making, and sense-sharing processes to make tacit knowledge explicit are key processes that break down the problem into components, enabling their understanding and integrating them into a solution proposal. Based on this, it is possible to involve and allow citizens to take part in the governance of processes from a strategic and political perspective, moving towards a new understanding of participation. Applying the systemic design methodological approach, researchers address the complexity of citizens’ relationship with information, allowing users to become active and not only informed about environmental management activities in the territory, reducing socio-environmental conflicts. This paper discusses the contribution of systems-oriented design as a relevant methodology to address the process of problem framing, need-finding, ideation, prototyping, testing and iteration in complex projects, where the EO acts as an example. It is concluded that the contribution of SOD was present in (a) the modelling of the complexity of the problem, incorporating the vision of each actor of the system, (b) the internal articulation of the multidisciplinary team of researchers to reach a single interdisciplinary result—the EO platform, and (c) the synthesis of new knowledge that allows the creation of a transdisciplinary methodological strategy.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".