Adaptive social impact management for conservation and environmental management
Bibliographic record
Abstract
Concerns about the social consequences of conservation have spurred increased attention the monitoring and evaluation of the social impacts of conservation projects. This has resulted in a growing body of research that demonstrates how conservation can produce both positive and negative social, economic, cultural, health, and governance consequences for local communities. Yet, the results of social monitoring efforts are seldom applied to adaptively manage conservation projects. Greater attention is needed to incorporating the results of social impact assessments in long-term conservation management to minimize negative social consequences and maximize social benefits. We bring together insights from social impact assessment, adaptive management, social learning, knowledge coproduction, cross-scale governance, and environmental planning to propose a definition and framework for adaptive social impact management (ASIM). We define ASIM as the cyclical process of monitoring and adaptively managing social impacts over the life-span of an initiative through the 4 stages of profiling, learning, planning, and implementing. We outline 14 steps associated with the 4 stages of the ASIM cycle and provide guidance and potential methods for social-indicator development, predictive assessments of social impacts, monitoring and evaluation, communication of results, and identification and prioritization of management responses. Successful ASIM will be aided by engaging with best practices - including local engagement and collaboration in the process, transparent communication of results to stakeholders, collective deliberation on and choice of interventions, documentation of shared learning at the site level, and the scaling up of insights to inform higher-level conservation policies-to increase accountability, trust, and perceived legitimacy among stakeholders. The ASIM process is broadly applicable to conservation, environmental management, and development initiatives at various scales and in different contexts.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".