"Don't be too proud of this technological terror you've constructed"
Bibliographic record
Abstract
Transhumanism is a school of thought that promotes the enhancement of humanity through technological intervention (e.g., cloning, gene therapies, uploading one’s mind to a computer, nanotechnology). Due to its aims of altering evolutionary processes (Bostrom, 2005), transhumanism is highly controversial (Sinicki, 2015). The ideology finds support from younger men, as well as those engaged in science-fiction literature (Gangadharbatla, 2020; Koverola et al., 2022). The present study aimed to investigate the role of gender and specific science fiction fan identities as predictors of transhumanism in three different samples of fandoms affiliated with science-fiction (e.g., anime fans, furries, and Star Wars fans) as well as in a control sample of college students. Participants (N = 6840) responded to a novel measure of transhumanist orientation in either an online or in-person survey. The findings indicated that men were the most likely to endorse transhumanism, as were fans of Star Wars and furries. Overall, the present study supports theorizing that transhumanism may be an influential motif in the science-fiction genre, as well as an appealing ideology for men.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".