Scaling the impact of sustainability initiatives: a typology of amplification processes
Bibliographic record
Abstract
Abstract Amplifying the impact of sustainability initiatives to foster transformations in urban and rural contexts, has received increasing attention in resilience, social innovation, and sustainability transitions research. We review the literature on amplification frameworks and propose an integrative typology of eight processes, which aim to increase the impact of such initiatives. The eight amplification processes are: stabilizing, speeding up, growing, replicating, transferring, spreading, scaling up, and scaling deep. We aggregated these processes into three categories: amplifying within, amplifying out, and amplifying beyond. This integrative typology aims to stimulate the debate on impact amplification from urban and rural sustainability initiatives across research areas to support sustainability transformations. We propose going beyond an understanding of amplification, which focuses only on the increase of numbers of sustainability initiatives, by considering how these initiatives create transformative change.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".