An integrative framework for transformative social change: a case in global wildlife trade
Bibliographic record
Abstract
To achieve a sustainable future, it is imperative to transform human actions collectively and underlying social structures. Decades of research in social sciences have offered complementary insights into how such transformations might occur. However, these insights largely remain disjunct and of limited scope, such that strategies for solving global environmental challenges remain elusive. There is a need to integrate approaches focusing on individuals and social structures to understand how individual actions influence and are in turn influenced by social structures and norms. In this paper, we synthesize a range of insights across different schools of thought and integrate them in a novel framework for transformative social change. Our framework explains the relationships among individual behaviors, collective actions, and social structures and helps change agents guide societal transitions toward environmental sustainability. We apply this framework to the global wildlife trade—which presents several distinct challenges of human actions, especially amidst the Covid-19 pandemic—and identify pathways toward transformative change. One key distinction we make is between different individual actions that comprise the practice itself (e.g., buying wildlife products; private action ) and those that push for a broader system change in practice (e.g., signaling (dis)approval for wildlife consumption; social-signaling action , and campaigning for policies that end unsustainable wildlife trade; system-changing action ). In general, transformative change will require an integrative approach that includes both structural reforms and all three classes of individual action.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.069 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".