Why we put on the sorting hat: motivations to take fan personality tests
Bibliographic record
Abstract
Background There is little reason to believe that fan-related personality tests, which tell fans what type of person they are based on their fa-vorite fan content, are valid or reliable. Nevertheless, fan-related personality tests remain incredibly popular online. Participants and procedure Building upon existing fan research, the present study tests whether fans may have other motivations for taking such personality tests, drawing upon prior research by Wann. Self-identified fans (N = 425, Mage = 26.41, SD = 8.44) completed measures re-garding degree of identification with their fan interest, motivations to take fan quizzes, and frequency of taking fan personality quizzes. Results Highly-identified fans were found to more frequently take fan-related personality tests, an association significantly mediated by both self-esteem and escapism motivations, but not mediated by eustress, entertainment, belongingness, or family motivations. Conclusions The results suggest that highly identified fans participate in more fan personality quizzes to escape from the hassles of everyday life and increase positive self-worth. These results are discussed with respect to their theoretical relevance – both for research on personality testing and on fan activities – as well as for their practical implications.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".