Bibliographic record
Abstract
The “particular cultural and political circumstances in which i write” have changed decisively several times in my life. I have been several times an expatriate and finally an émigré from Yugoslavia, and I am now an expatriate from Canada—a life that has made me very attuned to global material and moral changes. I shall focus on the changes that I personally felt as inciding deeply into my professional work in the last ten years or so, the watershed for me being the illegal and immoral bombing of Serbia led by the United States. By then I had published three books on science fiction—Metamorphoses of Science Fiction (1979), Victorian Science Fiction in the U.K. (1983), and Positions and Presuppositions in Science Fiction (1988)—and written enough further essays for a fourth book, which was scrapped when the Liverpool University Press had its budget cut. The changes make me, alas, the bearer of bad news, for as I see it our rulers have in practice destroyed the wall that our disciplines wrongly thought existed between culture and political economics, and we had better draw the consequences. And yet the gesture and bearing (see Suvin, “Haltung”) of writing this report also imply a hope that with much clarity, work, and luck we intellectuals—writers and then critics—can make an important difference.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".