Averting lock-in risks in social-ecological systems: a roadmap for pluralistic research and governance
Bibliographic record
Abstract
Although sustainability pathways are urgently needed to safeguard human and non-human life within a habitable Earth system, there is no consensual knowledge about how to trigger such pathways, due to differences in values, interests, and power at multiple levels. While we develop actionable knowledge about how to induce sustainability pathways in an equitable and just way, we need to better comprehend how to avoid “locked-in” outcomes—strongly entrenched situations minimizing potential for change—in social-ecological systems (SES). Here, we offer a roadmap to guide innovative research to observe, understand, and ultimately avoid, processes that can induce lock-ins at multiple spatiotemporal scales in SES. We use recent research illustrating lock-in dynamics in the Guadalquivir Estuary and Doñana Delta SES to funnel experiences and insights from other cases and produce a knowledge base comprehensive enough to highlight novel research avenues. Our roadmap is organized in three broad research areas: (1) general cross-cutting research themes; (2) policy, collective action and governance; (3) plurality of values and methodological pluralism, acknowledging the role of long-term socio-ecological research (LTSER) platforms as transdisciplinary knowledge co-production spaces supporting solution creation. Hopefully, this roadmap may serve to support solutions to collectively learn how to navigate away from lock-in in SES, towards safer and more equitable sustainability pathways.
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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 | 0.111 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.013 | 0.077 |
| Scholarly communication | 0.032 | 0.067 |
| Open science | 0.009 | 0.037 |
| Research integrity | 0.021 | 0.023 |
| Insufficient payload (model declined to judge) | 0.014 | 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".