The ‘boomerang effect’: insights for improved climate action
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
States have been negotiating climate mitigation actions centred around greenhouse gas emissions for several decades. In the wake of the Paris Agreement, a significant body of research has emerged reflecting on the unintended negative consequences of climate mitigation action. More recently, this research includes a focus on climate adaptation actions. The negative impacts have, together, been labelled ‘maladaptation’. Maladaptation as articulated in the literature takes many forms: e.g. displacement of communities from traditional lands such as forests and pasture, violent conflict at different scales, resource capture by elites. In this article, we argue in support of a careful delineation between local-level side effects of climate action and negative effects reaching back to the state (through different pathways and at different levels). The latter we label ‘boomerang effects’. We illustrate, through several examples, the pathways leading from climate action to local impact to boomerang effect, arguing that careful articulation of policy and program decisions, actions and effects upon the state provide support for improved policy making. Climate action is necessary, and necessarily must be better informed in order to achieve the broadest socio-ecological benefits possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| 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