Bibliographic record
Abstract
The world of the soap opera is centred around its fans, primarily their memories and the in-universe history they take pride and care in knowing and understanding. Using a piece of archival audiovisual (AV) footage from the ABC soap opera General Hospital, I discuss how the footage is used to engage and appeal to fans, who act as archivists, historians, and critics of the program. Using archival and library and information science (LIS) papers, I then situate the fan within these roles and as information keepers, drawing on the work of Baym (2000), Wilson (2012), Steuer (2019), Levine (2021) and Price and Robinson (2016). The existing literature does not address data loss, specifically in terms of fans as information sources and their archival practices on social media, which are ephemeral. I attempt to highlight this gap by exemplifying the important role that fans hold in fan spaces. I then revisit the archival clip and use a fan-moderated archive further explaining the fan’s important role as an information keeper and their significance in keeping the soap’s history alive. My personal response to the archival material functions as a case study in fan information behaviours, and addresses the invaluable power of fan labour. I conclude by acknowledging how the trend of tribute episodes that memorialize performers and the characters they portrayed signals a further interest in and importance of archival material to communicate and honour the achievements and history of the program.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".