Structured decision-making shows broad support from diverse stakeholders for habitat conservation and restoration in Kenya’s Central Highlands
Bibliographic record
Abstract
The need for targeted restoration in regions where ecosystem integrity has become compromised is now widely recognised. Local community views, alongside those of other stakeholders, should be incorporated into transparent decision-making to ensure conservation/restoration activities are successful. We used a structured decision-making approach, working with stakeholders and local communities, to pose and answer the following question for Kenya’s Central Highlands: “ what future land-use options [2030] are feasible for the study region, which is most preferable, how does this vary between different stakeholder groups, and what values drive these preferences? ”. We engaged with 51 individuals from six stakeholder groups ( Big Farms , Conservationists , Counties , Forest Users , Pastoralists , Smallholders ). As individuals, the stakeholders held significantly different values for provisioning, cultural, regulation and maintenance ecosystem services. However, following consensus-building activities within the six groups, shared values and perspectives emerged. The future land-use option of habitat conservation/restoration was preferred by the majority of stakeholder groups, although one ( Big Farms ) favoured increased plantation forestry. Water resource management was also prioritised consistently. By using structured decision-making, we demonstrate that ecosystem restoration is compatible with the views and values of smallholders and forest users, as well as those with a direct interest in conservation. Structured decision-making processes can facilitate stakeholders with disparate views to work towards a consensus regarding future land-use options, aiding environmental planning and implementation. • Ecosystem restoration is needed for biodiversity and ecosystem function recovery. • Structured decision-making is a transparent way to account for stakeholder values. • Individuals in Kenya’s Central Highlands valued ecosystem services differently. • Water management and restoration emerged as priorities from consensus-building. • Structured decision-making helped those with disparate views reach near consensus.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".