Understanding the Use of Ecosystem Service Knowledge in Decision Making: Lessons from International Experiences of Spatial Planning
Bibliographic record
Abstract
The limited understanding of how ecosystem service knowledge (ESK) is used in decision making constrains our ability to learn from, replicate, and convey success stories. We explore use of ESK in decision making in three international cases: national coastal planning in Belize; regional marine spatial planning on Vancouver Island, Canada; and regional land-use planning on the island of Oahu, Hawaii. Decision makers, scientists, and stakeholders collaborated in each case to use a standardized ecosystem service accounting tool to inform spatial planning. We evaluate interview, survey, and observation data to assess evidence of ‘conceptual’, ‘strategic’, and ‘instrumental’ use of ESK. We find evidence of all modes: conceptual use dominates early planning, while strategic and instrumental uses occur iteratively in middle and late stages. Conceptual and strategic uses of ESK build understanding and compromise that facilitate instrumental use. We highlight attributes of ESK, characteristics of the process, and general conditions that appear to affect how knowledge is used. Meaningful participation, scenario development, and integration of local and traditional knowledge emerge as important for particular uses.
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.037 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".