Post-growth and the lack of diversity in the scenario framework
Bibliographic record
Abstract
Scenarios and pathways, as defined in the SSP-RCP framework, are central to recent climate research and the latest IPCC report. Shared Socioeconomic Pathways (SSPs) offer a small set of alternative futures through qualitative narratives and quantitative projections. A key use of SSP-based scenarios is mitigation analysis, presenting decision-makers with a seemingly neutral set of policy options. However, all SSPs assume continuous global economic growth (GEGA) through 2100, effectively narrowing the solution space and embedding value-laden assumptions that challenge the IPCC’s claim to policy neutrality. Post-growth scholars contest GEGA, but post-growth mitigation scenarios have yet to be fully integrated into this scenario framework. From a philosophy of value-laden science perspective, I argue that this integration is necessary. I propose two approaches to do so, demonstrating how inclusion of post-growth scenarios aligns with a diversity criterion — ultimately enhancing the framework’s objectivity and strengthening the IPCC’s policy neutrality.
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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 | 0.063 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| 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".