Bibliographic record
Abstract
Literary and cultural texts are essential in shaping emotional and intellectual dispositions toward the human potential for a sustainable transformation of society. Due to its appeal to the human imagination and human empathy, literature can enable readers for sophisticated understandings of social and ecological justice. An overabundance of catastrophic near future scenarios largely prevents imagining the necessary transition toward a socially responsible and ecologically mindful future as a non-violent and non-disastrous process. The paper argues that transition stories that narrate the rebuilding of the world in the midst of crisis are much better instruments in bringing about a human “mindshift” (Göpel) than disaster stories. Transition stories, among them the Parable novels by Octavia Butler and Kim Stanley Robinson’s The Ministry for the Future (2020), offer feasible ideas about how to orchestrate economic and social change. The analysis of recent American, Canadian, British, and German near future novels—both adult and young adult fictions—sheds light on those aspects best suited for effecting behavioral change in recipients’ minds: exemplary ecologically sustainable characters and actions, companion quests, cooperative communities, sources of epistemological innovation and spiritual resilience, and an ethics and aesthetics of repair.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".