Time for tools: A review on geospatial tools and their role in co‐management
Bibliographic record
Abstract
The overall goal of this study was to conduct an extensive literature review to characterize how geospatial tools are being applied in co‐management contexts globally. This was accomplished through two objectives: (i) to investigate the use and applications of geospatial tools in cases of natural resource co‐management; and (ii) to identify benefits and challenges associated with the use of these technologies. A total of 26 articles met the inclusion criteria for review; these encompassed a range of contexts, but were predominantly focused on co‐management of fisheries, protected areas, and forests. Case studies were analyzed through the lens of four tool‐based categories: participatory mapping, spatial data collection, remote sensing, and modelling. Findings suggest a wide range of applications, demonstrating the versatility of these tools including those used to measure the efficacy of co‐management. Frequently cited geospatial tool benefits included identifying space‐use patterns, determining the state of resources, capacity building, and monitoring change over time. Challenges with geospatial tools included issues of scale and generalization, data uncertainty, incomplete or poor data, and lack of trust. A primary conclusion from this study is that geospatial tools tend to be used in a participatory fashion, thereby contributing to key elements of co‐management such as trust building and knowledge co‐production.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".