What factors enable social-ecological transformative potential? The role of learning practices, empowerment, and networking
Bibliographic record
Abstract
Achieving sustainability in the Anthropocene requires radical changes to how human societies operate. The Seeds of Good Anthropocenes (SOGA) project has identified a diverse set of existing initiatives, called “seeds,” that have the potential to catalyze transformations toward more sustainable pathways. However, the empirical investigation of factors and conditions that enable successful sustainability transformations across multiple cases has been scarce. Building on a review of existing theoretical and empirical research, we developed a theoretical framework for assessing three features identified as important to transformative potential of innovative social-ecological initiatives: (1) learning practices, (2) empowerment, and (3) networking. We applied this framework to a set of African-led and Africa-related initiatives that we selected from the SOGA database that were divided into initiatives with more or less transformative potential. We coded the presence or absence of features relating to the theoretical framework using secondary data, and then compared the initiatives using qualitative comparative analysis (QCA). This analysis revealed that of the three features tested, Networking emerged as the most important feature for transformative potential when compared amongst cases. By developing and testing a framework for the comparison of cases we provide a basis for future comparative work to further identify and test properties of cases that enable transformation.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".