Bibliographic record
Abstract
This study analyzes the presence of urban space in 20th century science fiction in English using computational methods. Three theoretical approaches are used to model urban space as a measurable feature. First, urban space is formalized as a topic. LDA topic modeling is used to retrieve the urban topic from the corpus and estimate its presence in each book. Secondly, urban space is formalized as the sum of the linguistic fragments that form a setting. A list of urban terms is created and their frequency is measured for each novel. Lastly, cityspace is formalized as the number of references to urban locations. Textual Geographies’ geographic data was used to measure the presence of named urban locations in each book. The results of these approaches all point to similar conclusions. A low presence of urban space is found in science fiction compared to general fiction, alongside a historical trend. Urban presence in science fiction is greater at the beginning of the 20th century, declines in the 30s and 40s, and successively increases in the 50s. No such dip is present in other types of fiction across the twentieth century.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".