Amplifying “Keep It in the Ground” First-Movers: Toward a Comparative Framework
Bibliographic record
Abstract
This article offers a framework for analyzing and extending the recent wave of national “keep it in the ground” (KIIG) bans on fossil fuel exploration and production. We situate this discussion in new theoretical work on decarbonization acceleration and then present an overview of KIIG movement and policy development. Next, drawing on the burgeoning supply side climate policy literature, we outline major barriers to constraining fossil fuel development, then focus on identifying conditions most conducive for KIIG policy. These include locally-rooted campaigns, the development of a pro-KIIG constituency that is horizontally dense and vertically integrated, resonant message framing, and support by well-placed norm entrepreneurs. We argue that early national efforts to keep fossil fuels in the ground demark a critical juncture in global climate policy. Understanding the trajectory of these bans is a first step in extending these initiatives as part of the pathway to carbon neutrality by 2050.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".