Revolutionizing the imaginary: Visions for a degrowth future
Bibliographic record
Abstract
My research tries to answer the question: Can imagining a degrowth future contribute to unlearning capitalist realism? Degrowth is based on the understanding that the earth’s resources are finite, and that sustaining economic growth, even if under the guise of ‘green growth’ or ‘eco-capitalism,’ will continue to lead to catastrophic consequences. Beyond advocating for the equitable reduction of production and consumption, degrowth challenges the narrative that growth equals progress, or that it is essential for ‘development’ or wellbeing. Nevertheless, one of the biggest challenges the degrowth movement faces, is overcoming capitalist realism, or the pervasive belief that capitalism is the only viable economic and political system. I argue that speculative fiction presents a powerful tool for this purpose. Authors have long used speculative fiction to explore possible futures—whether utopian, dystopian, or somewhere in between. By integrating the degrowth goals and visions with nonWestern perspectives on relational collectivity, pluriversity, and inter-species reciprocity, my research uses speculative fiction as a thought experiment in imaging a degrowth alternative to capitalism.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".