Bibliographic record
Abstract
The purpose of this text is to reflect on the ways that science fiction allows criticism on the modern technology path. Imagination has allowed us to think of some ends of the world, but it has been a privileged space. Creating other possible futures for our relationship with energy is essential. Corporate renewable energy projects are emerging in corners of the planet where green capitalism has not yet reached. In this way, the creation of alternatives to centralized and corporate models of energy generation, distribution and consumption must go through new potentialities of inhabiting new possible futures. Science fiction is a literature genre that has inspired generations of people assembling art and techno-science as well as dystopia. Solarpunk has been consolidated as a space of counter-cultural hope to allow us to go beyond social-ecological injustices and growing epistemic and ontological violence. This genre is derived from other currents such as Cyberpunk, Steampunk and Dieselpunk, elucidating another relationship between technology, society and nature, nourished in turn by climate sci-fi, Indigenous and Afro-futurist science fiction. In this sense, a concept revision is made in three spheres: i) historical, based on its digital origins; ii) literary, based on the edited anthologies and iii) academic, of the reflections that it has raised. Finally, the Solarpunk Manifesto, revealed at the beginning of 2020, is shared in order to continue its co-creation.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".