Community information systems based on unified modelling language to create a participatory monitoring tool on the environmental impact of agriculture
Bibliographic record
Abstract
Community Information Systems (CIS or observatoire du territoire in French) were designed to monitor environmental change in given agriculture-dominated areas. They serve both as information resource centres and as fora for exchange and coordination among local stakeholders. In 2004, the French Agriculture and Fishing Ministry asked CIRAD (Agricultural Research Center for Developing Countries) to assist in the set-up of CISs that would serve as pilot studies. CISs are co-constructed by local stakeholders using Unified Modelling Language to define and structure the information needed to better understand a specific problem associated with agriculture. Two years of experience of setting up CISs has allowed us to develop methods and recommendations for the set-up of such community information systems. We identify the factors that favour local and collective ownership of a social device based on data management, an area traditionally perceived as the domain of experts, i.e., "off-limits" to non-expert local stakeholders. We discuss the extent to which CISs allow a diversity of stakeholder groups to collectively address the multiple perceptions and interests and to tackle the complexities inherent in the agriculture-environment relationship.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".