Assembling, channeling, and orienting watershed management: The performative roles of computer models in environmental management institutions
Bibliographic record
Abstract
Large-scale watershed management increasingly depends on the use of computational models to inform decision-making and track management goals; however, the roles that models play in environmental management institutions far exceed their informational content. Science studies scholars have approached modelling as also a performative practice that shapes the relational context of watershed management. Drawing on an ethnographic approach, this article examines a single computer model as it is developed and deployed in an environmental management organisation. The study shows that a single model can serve multiple roles within a watershed management institution depending on specific conditions and contexts; further, by serving these multiple roles rather than a single informational one, models are uniquely useful for organising environmental science and management practices and institutions across a heterogeneous set of agents. Examining these multiple roles can help us to understand not only the process of computational modelling, but also the process of management and how different organisations can coordinate with one another through the use of modelling.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".