Acculturation strategies as predictors of fandom identification in the fanfiction,<i>Star Wars</i>fan, and furry communities
Bibliographic record
Abstract
Research suggests that people at the interface of two different cultures may face a dilemma regarding how or whether to adopt aspects of the new culture in light of their existing cultural identity. A growing body of research in fan communities suggests that similar group processes may operate in recreational, volitional identities. We tested this by examining the associations between acculturation attitudes and identification with fan communities across three studies. Fanfiction fans, Star Wars fans, and furries completed measures of four different acculturation strategies with respect to managing their fan and non-fan communities as well as a measure of their identification with the fan community. Results across the three studies consistently found that integration and assimilation strategies positively predicted fan community identification, while separation and marginalization strategies negatively predicted fan community identification. Together, the results conceptually replicate and find evidence for the acculturation model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".