Deliberate decline: An emerging frontier for the study and practice of decarbonization
Bibliographic record
Abstract
Abstract Promoting low‐carbon innovation has long been a central preoccupation within both the practice and theory of climate change mitigation. However, deep lock‐ins indicate that existing carbon‐intensive systems will not be displaced or reconfigured by innovation alone. A growing number of studies and practical initiatives suggest that mitigation efforts will need to engage with the deliberate decline of carbon‐intensive systems and their components (e.g., technologies and practices). Yet, despite this realisation, the role of intentional decline in decarbonization remains poorly understood and the literature in this area continues to be dispersed among different bodies of research and disciplines. In response, this article structures the fragmented strands of research engaging with purposive decline, interrogating the role it may play in decarbonization. It does so by systematically surveying concepts with particular relevance for intentional decline, focusing on phase‐out, divestment, and destabilization. This article is categorized under: Decarbonizing Energy and/or Reducing Demand > Decarbonizing Energy and/or Reducing Demand
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.
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 | 0.039 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.139 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".