Morphological Conservation in Human-Animal Hybrids in Science Fiction and Fantasy Settings: Is Our Imagination as Free as We Think It Is?
Bibliographic record
Abstract
The question of whether human imagination knows no boundaries or is, alternatively, constrained by conscious or unconscious cognitive templates is a key issue in defining human mind. We try here to address this extremely large question by focusing on one particular element of imaginary creations, the specific case of human-animal hybrids. Human-animal hybrids are common inhabitants of human imaginary spaces, being regularly encountered across numerous mythologies, as well as in modern popular culture. If human imagination was unconstrained, it would be expected that such hybrid creatures would display roughly half human and half animalistic features. Using several different popular science fiction and fantasy settings, we conducted an analysis of the morphological traits of human-animal hybrids, both anatomical and phenotypic. Surprisingly, we observed extremely high conservation of human morphological traits in human-animal hybrids, with a contrasting high use of phenotypic (“cosmetic”) alterations, and with highly stereotyped patterns of morphological alterations. While these alterations were independent of the setting considered, shape alterations were setting-dependent and used as a way to increase internal coherence. Finally, important gender differences were observed, as female human-animal hybrids retained significantly more human traits than males did, suggesting that conservation of female appearance may bear essential evolutionary importance. Taken together, these results demonstrate the existence of strong cognitive templates which frame and limit the expression of the capacity of human imagination, and unveil some of the psychological mechanisms which constrain the emergence of imaginary spaces.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".