Systems thinking for planning and evaluating conservation interventions
Bibliographic record
Abstract
Abstract As conservation shifts to meet the challenges of our globalized world, approaches for planning and evaluating interventions must evolve to account for the increasing complexity of conservation problems and the dynamic, multiscalar relationships between humans and the environment. Systems thinking offers approaches that could help conservation be more adaptive, transparent, and evidence‐based. Using case studies and the literature, we trace the evolution of systems thinking and demonstrate how systems mapping could support the process of planning and evaluating interventions. Systems mapping helps disentangle the context of conservation and encourage collaborative planning that integrates diverse views. It can also change the way interventions are characterized and communicated by emphasizing the systems targeted for change as opposed to actions. Last, it can encourage evidence‐based decision‐making by identifying indicators attune to complexity, prompting discussion on knowledge gaps, and filling gaps through qualitative mapping or computational modeling. Integrating systems thinking in practice will help practitioners foster the capacity for learning and adaptation required for conservation to deliver global results.
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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 | 0.079 | 0.112 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 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".