Bibliographic record
Abstract
Science fiction stories of pandemic-inspired ingenuity, grit, and determination. This new volume in the Twelve Tomorrows series of science fiction anthologies looks at how science and technology—existing or speculative—might help us create a more equitable and hopeful world after the coronavirus pandemic. The original stories presented here, from a diverse collection of authors, offer no miracles or simple utopias, but visions of ingenuity, grit, and incremental improvement. In the tradition of inspirational science fiction that goes back to Isaac Asimov and Arthur C. Clarke, these writers remind us that we can choose our future, and show us how we might build it. In these imagined futures, telepresence tourism replaces the viral dangers and environmental destruction of international travel; hackers attempt to disrupt the new quadratic voting system; robot bartenders administer vaccines; a Canadian farmer grows grain for the national rationing program; Hong Kong refugees create an augmented reality performance space for the Edinburgh Festival; a worker must choose between his daughter and his job caring for the people and environment of the locked-down and rewilded Kolkata. In addition, Wade Roush, science writer and editor of a previous Twelve Tomorrows anthology, interviews Ytasha Womack, author of Afrofuturism and Post Black, about the pandemic, racial justice, and how science fiction can help us imagine a healthier, fairer society. Stories by Madeline Ashby, Indrapramit Das, Cory Doctorow, Adrian Hon, Rich Larson, Ken Liu, Malka Older, Hannu Rajaniemi, Karl Schroeder, D. A. Xiaolin Spires Interview Wade Roush, Ytasha Womack
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.604 | 0.422 |
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".