Bibliographic record
Abstract
This article analyzes Charles de Lint’s 1989 novel Svaha as an example of how distinct national identities can endure in the globalized future espoused by most cyberpunk texts. Instead of imagining a generic urban sprawl in which it is increasingly difficult to maintain a stable social or communal identity, Svaha addresses issues of Canadian identity based on the division of living spaces by various social and cultural boundaries. The article begins by assessing Istvan Csicsery-Ronay’s argument that science fiction shows little interest in the future of nations, a notion I counter by means of Anthony D. Smith’s claims for nationality as an enduring connection to history (time) and homeland (space) that extends both before and after the current political incarnation of the nation-state. The article then offers a reading of the segregated urban space in Svaha as a response to the legacy of Canada as a settler colony that prioritizes immigrant identities over First Nations identities. In the novel, the mosaic model of Canadian multiculturalism that resulted in the fragmentation of the country is replaced by a more integrated model based on a First Nations land ethic. The article ends by considering some of the problems with the optimistic conclusion of the novel, in which space is used to overcome historical legacies of dispossession.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.020 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".