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Storytelling and participatory system dynamics modelling for water resources management in Lake Atitlán of Tz'olöj Ya' in Mayan Guatemala

2020· article· en· W3091945987 on OpenAlexaff
Julien Jean Malard-Adam, Jessica Bou Nassar, Jan Adamowski, Marco Ramírez Ramírez, Héctor Tuy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Cultural Studies in Latin America and Beyond
Canadian institutionsMcGill University
Fundersnot available
KeywordsStorytellingContext (archaeology)Citizen journalismNarrativeSociologyGeographyEcologyEnvironmental resource managementPolitical scienceEnvironmental scienceBiologyLaw

Abstract

fetched live from OpenAlex

Participatory system dynamics modelling is a useful tool for sociohydrological systems management due to its inclusion of diverse viewpoints and incorporation of feedback dynamics and delays between the human and environmental spheres. We here present a case study from the Lake Atitlán watershed in Guatemala, which is unique due to its endorheic nature, very long retention time, and diversity of human societies around it (Kaqchikel, Tz’utujil and K’iche’, as well as a Hispanic minority). The lake is under pressure from several sources and has become increasingly vulnerable to eutrophication in recent years. The lake is also central to the economy and ecology of the region, with diverse stakeholders including fishers, farmers, both traditional and youth-led Mayan organisations, NGOs, businesses, and municipalities and other levels of government. While effectively all participating stakeholders agree that the lake is under threat, there exist very differing narratives regarding the most pressing threat (pollution, biodiversity, or water availability) and therefore appropriate policy options. These differences vary significantly according to the ecosystem services each stakeholder obtains from the lake, as well as their own personal experiences and worldviews. Indigenous voices have also unfortunately been historically marginalised and often excluded from decision-making in environmental management. In this context, we applied a novel methodology incorporating storytelling and narratives coupled with causal loop diagrams to incorporate the points of view of all stakeholders, whether literate or not. The results from these individual interviews were used to compare visions and possible solutions, followed by the development of a coupled human-hydrological systems model as a decision support tool. In the coupled model development process, socioeconomic processes are represented in a system dynamics model, while hydrological processes are eventually "outsourced" to an external hydrological model (such as SWAT+). Using the Tinamït software package, these two models can then be simultaneously executed with data (e.g., land use and water quality) dynamically exchanged between both models at runtime. While most studies conducted in or on Indigenous regions and their peoples are conducted in European languages that exclude these very people from meaningful decision-making, all team members (both national and international) in this research project were chosen to be functional in at least one of the mutually intelligible Mayan languages spoken in the basin, and these languages were used as the official project language (while also providing services in Spanish for Hispanic stakeholders). This key aspect to our approach ensured that all stakeholders were equally included in the process, and that Indigenous students also had equal opportunities to be hired as part of the (decision-making) research team. We discuss how this methodology led to unique contributions to the model throughout the research process, from problem definition to identification of key system processes and candidate policy scenarios, and improved the quality of both the participatory and the modelling processes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.041
GPT teacher head0.238
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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