Bibliographic record
Abstract
Deux conceptions differentes du fan sont vehiculees dans les discours sociaux sur la culture populaire: la figure du fan excessif et celle du fan serieux. Cet article questionne le bien-fonde de cette distinction, en analysant le meme type de pratique (soit l'accumulation de savoirs via les sites Web), selon une organisation commune (la communaute de fans). Des sites Web de deux communautes de fans, celles de Norah Jones et de Kylie Minogue, ont ete etudies. Les sites de ces deux communautes Web comptent generalement les memes offres de contenu pour les deux chanteuses. Celles-ci demontrent des savoirs considerables, manifestes et latents, de la part des fans qui concoivent ces sites et qui font partie de leur communaute Web. A travers tous ces types de savoirs mobilises, il est possible de constater que les fans, qu'ils fassent partie des « excessifs » ou des « aficionados », consacrent une energie considerable afin d'accumuler un impressionnant volume de savoirs autour de leur idole.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".