Science fiction: źródła i kontynuacje
Bibliographic record
Abstract
A discussion addresses general problems associated with studying science fiction narratives today. What does it take to make a believable illusion of the scientific out of literary fiction? What are the most contemporary views on the genre and its multiple iterations? Is it still a genre? These and many more questions have been tackled below by the leading experts in science fiction studies: Paweł Frelik (University of Warsaw), the most prominent Polish theorist in the field, editor of the „Journal of Gaming and Virtual Worlds”, and author of Visual Cultures of Science Fiction (2017, reviewed in this issue), as well as the first Polish president of Science Fiction Research Association (2013-2014); Paul Kincaid, renowned science fiction critic, author of A Very British Genre: A Short History of British Fantasy and Science Fiction (1995) and What It Is We Do When We Read Science Fiction (2000); Lisa Swanstrom (University of Utah), co-editor of „Science Fiction Studies”, and author of Animal, Vegetable, Digital: Experiments in New Media Aesthetics and Environmental Poetics(2016); Sherryl Vint (University of Alberta), also co-editor of „Science Fiction Studies”, director of Science Fiction and Technoculture Studies at University of California, Riverside, and co-editor of The Routlege Companion to Science Fiction (2009); and, finally, „Creatio Fantastica” editors—Krzysztof M. Maj, Mateusz Tokarski, and Barbara Szymczak-Maciejczyk.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".