From Science as Solution to Science as Suspect: : The Human-Science Relationship in Science-Fiction Canon
Bibliographic record
Abstract
The ways in which humankind relates to and innovation has always been a key marker of the science-fiction genre. Though this relationship was popularly rooted in scientific rationalism and proto-idealism, it has since evolved in favour of problematizing relations between the human and the machine. Drawing on the work of authors such as Isaac Asimov, Frank Herbert, Philip K. Dick, Jeff Somers, and Iain Reid, this paper is a genre-oriented exploration of the shifting dialogue on how humankind should orient itself toward technological progress. Starting in the era of 1950's fiction, as epitomized by Asimov, the literary endorsement of science as solution has veered to science as suspect. Expressed first through the complication of the human-science relationship in transitory works, this shift in canonical discourse is readily captured in Herbert's Dune and Dick's Do Android's Dream of Electric Sheep with Somers' The Final Evolution and Reid's Foe demonstrating a contemporary finalizations of this trend. Department: English Faculty Mentor: Dr. William Thompson
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".