Bibliographic record
Abstract
What methodologies are required for auto/biography studies to take the popular seriously? This paper will offer a methodology for reading auto/biographical discourse and narrative in a medium of alternative media production, the personal zine. The methodology exemplified in this paper will seek to find synthesis between Gabrielle Helm's call for seriousness and Chris Atton's assertion that, in the case of much alternative media, “mundanity is all there is.” While canonized forms of auto/biography encourage the contemplation of the exceptional, an engagement with the popular inevitably involves an encounter with the banal. Offering an interpretation of the use of lists in a selection of personal zine narratives, I will examine different types of lists and their deployment as a discourse of autobiography. The list will be positioned as means of gesturing both towards the author's possession of particular (sub)cultural capital and as a narrative strategy used to acknowledge the complex dynamics which exist between the self and the popular.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".