Disrupting the opportunity narrative: navigating transformation in times of uncertainty and crisis
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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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.030 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| 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".