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Record W2941587160 · doi:10.5751/es-10586-240209

A systems thinking approach for eliciting mental models from visual boundary objects in hydropolitical contexts: a case study from the Pilcomayo River Basin

2019· article· en· W2941587160 on OpenAlexaffvenue
Riveraine S. Walters, Erin S. Kenzie, Alexander E. Metzger, William Jesse Baltutis, Kakali B. Chakrabarti, Shana Lee Hirsch, Bethany K. Laursen

Bibliographic record

VenueEcology and Society · 2019
Typearticle
Languageen
FieldComputer Science
TopicCognitive Science and Mapping
Canadian institutionsCentre for International Governance InnovationUniversity of Victoria
FundersNational Socio-Environmental Synthesis CenterU.S. Department of AgricultureNational Science Foundation
KeywordsBoundary (topology)Structural basinMental modelDrainage basinGeographyEcologyComputer scienceSociologyPsychologyCognitive scienceGeologyCartographyMathematicsGeomorphologyBiology

Abstract

fetched live from OpenAlex

Walters, R. S., E. S. Kenzie, A. E. Metzger, W. J. Baltutis, K. B. Chakrabarti, S. L. Hirsch, and B. K. Laursen. 2019. A systems thinking approach for eliciting mental models from visual boundary objects in hydropolitical contexts: a case study from the Pilcomayo River Basin. Ecology and Society 24(2):9. https://doi.org/10.5751/ES-10586-240209

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)0.007
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0050.005
Scholarly communication0.0030.004
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.278
Teacher spread0.256 · 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 designQualitative
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

Citations7
Published2019
Admission routes2
Has abstractyes

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