Aca-fans and fan communities: An operative framework
Bibliographic record
Abstract
Fan communities represent a major interest for researchers of consumer culture. However, their study has confronted scholars with a fundamental problem: how can one reconcile critical distance with being sufficiently integrated within a given fan community to gather reliable information? The phrase ‘aca-fan’ has become a familiar designation for scholars who are also fans. However, while the theoretical implications of the aca-fan’s posture have been widely discussed, conceptual, practical and methodological modalities remain to be unified. Shifting the focus away from strictly theoretical debates, we propose an operative framework for the role of the aca-fan. We consider the position of aca-fans as a node between academic and fan communities, familiar with both languages and therefore facilitating the process of integration of knowledge and take into account the relations that the aca-fans can have with the field, models and materials they collect, as well as the hierarchy between academic and fan sources of knowledge, providing practical suggestions to acknowledge various degrees of authority of fan voices. Finally, since aca-fans have an important control of, and responsibility for, the fields, models and data they study and the discourses they cite, the implications of aca-fans’ works for the perception of fan communities by society will be analysed. This article supports the fact that a rationalised position of aca-fans could not only be an optimal method to study communities of fan but also an intrinsically ethical way to approach these large communities of consumers.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.012 | 0.064 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".