Disrupting the opportunity narrative: navigating transformation in times of uncertainty and crisis
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
COVID-19 posed threats for health and well-being directly, but it also revealed and exacerbated social-ecological inequalities, worsening hunger and poverty for millions. For those focused on transforming complex and problematic system dynamics, the question was whether such devastation could create a formative moment in which transformative change could become possible. Our study examines the experiences of change agents in six African countries engaged in efforts to create or support transformative change processes. To better understand the relationship between crisis, agency, and transformation, we explored how they navigated their changed conditions and the responses to COVID-19. We document three impacts: economic impacts, hunger, and gender-based violence and we examine how they (re)shaped the opportunity contexts for change. Finally, we identify four kinds of uncertainties that emerged as a result of policy responses, including uncertainty about the: (1) robustness of preparing a system to sustain a transformative trajectory, (2) sequencing and scaling of changes within and across systems, (3) hesitancy and exhaustion effects, and (4) long-term effects of surveillance, and we describe the associated change agent strategies. We suggest these uncertainties represent new theoretical ground for future transformations research. Supplementary Information: The online version contains supplementary material available at 10.1007/s11625-023-01340-1.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it