On the discovery and enactment of positive socio-ecological tipping points: insights from energy systems interventions in Bangladesh and Indonesia
Bibliographic record
Abstract
Abstract Notions, such as leverage points, sensitive interventions, social tipping points, transformational tipping points, and positive tipping points, are increasingly attracting attention within sustainability science. However, they are also creating confusion and unresolved questions about how to apply these concepts when dealing with urgent global challenges such as rapid decarbonisation. We propose a relational methodology aimed at helping how to identify and support the emergence of positive ‘Social-Ecological Tipping Points’ (SETPs) that could bring about sustainability transformations. Our approach emphasises the need to pay attention to processes of social construction and to time dynamics. In particular, in a given social-ecological system, three key moments need to be considered: (1) The building of transformative conditions and capacities for systemic change, (2) A tipping event or intervention shifting the system towards a different trajectory or systems’ configuration, and (3) the structural effects derived from such transformation. Furthermore, we argue that the discovery and enactment of positive SETPs require considering multiple ontological, epistemological, and normative questions that affect how researchers and change agents define, approach, and assess their systems of reference. Our insights are derived from examining the implementation of household renewable energy systems at regional level in two rural areas of Indonesia and Bangladesh.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".