From Freud to Android: Constructing a Scale of Uncanny Feelings
Bibliographic record
Abstract
The uncanny valley is a topic for engineers, animators, and psychologists, yet uncanny emotions are without a clear definition. Across three studies, we developed an 8-item measure of unnerved feelings, finding that it was discriminable from other affective experiences. In Study 1, we conducted an exploratory factor analysis that yielded two factors; an unnerved factor, which connects to emotional reactions to the uncanny, and a disoriented factor, which connects to mental state changes more distally following uncanny experiences. Focusing on the unnerved measure, Study 2 tests the scale's convergent and discriminant validity, concluding that participants who watched an uncanny video were more unnerved than those who watched a disgusting, fearful, or a neutral video. In Study 3, we determined that our scale detects unnerved feelings created during early 2020 by the coronavirus pandemic; a distinct source of uncanniness. These studies contribute to the psychological and interdisciplinary understanding of this strange, eerie phenomenon of being confronted with what looms just beyond our understanding.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".