What factors enable social-ecological transformative potential? The role of learning practices, empowerment, and networking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it