Operationalizing pathway diversity in a mosaic landscape
Bibliographic record
Abstract
Understanding and building resilience is critical to responding to the deepening polycrisis. Pathway diversity is a promising approach to resilience that combines individual and systems perspectives, but so far has only been applied to idealized cases. Here, we use a rich case study from the mosaic landscape of Västra Harg, Sweden, to test and advance pathway diversity. Mosaic landscapes can simultaneously produce food, support biodiversity, and provide space for recreation, but these benefits require multiple actors to collectively and individually respond to changing circumstances. Our results indicate that, although the mosaic landscape provides many options for actors, forestry strategies are generally more resilient than agricultural strategies due to higher risks of abandonment in agriculture. We also found that supporting a specific strategy may create lock-in and undermine livelihood resilience overall. The study contributes toward developing a practical method for assessing resilience that can inform governance of complex social-ecological systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".