Toward an integrative framework for local development path analysis
Bibliographic record
Abstract
Despite decades of debate and policy interventions, the wicked social-ecological problem of anthropogenic climate change continues to threaten the sustainability of local communities. Impacts resulting from a rapidly changing climate are now inevitable yet variable in their nature and timing, depending on the extent to which local communities can respond. Transforming to low-carbon communities requires the ability to interrupt the inertia in existing development paths and shift these to more sustainable trajectories. Intervening in local development paths to mitigate climate change requires understanding the multilevel interactions between actors, practices, structures, and ecosystems implicated in system transformations. In this paper we explore the synergies between the multilevel perspective on transitions, social-ecological systems thinking, and social practice theories. We use these to conceptualize an integrative analytical framework capable of assisting researchers and local governments to understand how development path trajectories are sustained, and hence, which local climate initiatives are more likely to change the inertia of current development paths, and which ones only tinker at their edges.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".