The Multiverse as Theory in Postmodern Speculative Fictional Narratives
Bibliographic record
Abstract
The Multiverse as Theory in Postmodern Speculative Fictional Narratives considers the concept of the multiverse beyond the immediacy of being merely an excuse or scenario for the development of stories, instead positioning the multiverse as a theoretical method in which speculative fiction narratives can explore diverse issues to bridge ideas across cultural, social, and philosophical analysis. Taking a cross-cultural approach, the book centres around the critical engagements that literary and media texts have with the representations of the multiverse, beyond considering this subject as a mere rhetorical flourish or a passing fad. A diverse and international team of authors engage with the multiverse from the point of view of “other worlds,” understanding it not as the appearance of another independent world, but as the collision of two or more different worlds into one of them. From this key finding, the multiverse encourages us to pay attention to the influence that fiction exerts on narratives and world-building, providing possible frameworks to rethink critical aspects of temporality, space, self, society, and culture in contemporary times. This pioneering work will interest students and scholars working in the areas of media and cultural studies, comparative literature, popular culture studies, speculative fiction, and transmedia studies.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.083 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".