Islands in Speculative Fiction: The Functions of Islands in Science Fiction, Fantasy, and Horror Film and Writing
Bibliographic record
Abstract
The study of islands in speculative fiction intersects with cultural geography, political theory, and literary futurology. It unfolds within the concept of relational shifts between islands and continents, the deconstruction and reconstruction of the East-West binary, and the dynamic interplay between technology and human knowledge systems. As one of the most technologically and socially imaginative forms of literature, speculative fiction positions humanity within a vast coordinate system spanning the local, the global, and even the interstellar. In so doing, it examines and reflects upon the past, present, and future of human civilization—ultimately offering insights that may contribute to a more harmonious and sustainable future. Building on these themes, this special section explores how islands are represented and imagined in speculative fiction, including science fiction, to uncover the unique qualities that distinguish future-oriented island narratives from their traditional counterparts. The papers included here can be divided into three key thematic categories: (1) islands as liminal spaces for seeking identity and redemption, (2) islands as sites of ecological and economic crisis, and (3) islands as realms of colonial and utopian imaginations.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".