Bibliographic record
Abstract
This thesis is a novella whose protagonist--an erstwhile sideshow performer and habitual runaway--is involved in an exploration of discovery, in essence, a journey from becoming-to-being. The story begins at the protagonist's death, as she "relives" moments before she finally dies. The protagonist is followed by her guardian angel who traces his charge's steps in reverse: finding her, then determining where she's been. The protagonist encounters both adversaries and helpers, both causing her to deviate more than once from her path. The journey ends as the protagonist rebirths herself and returns to the site of her first voyage--this time making a decision to stay on that path, rather than return "home". The story is told in several voices including that of the protagonist herself; but more often, her facilitators are the narrators, each of whom, like the protagonist, are doomed to repeat history until they "get it right." The narration is mostly reported dialogue or epistolary excerpts each containing little description in order to portray a series of oral recountings or musings. Interspersed are excerpts from various chapters of a sideshow instructional manual. The form, ultimately, is more cerebral or ethereal, which serves as a mirror to the physicality of the content.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.796 | 0.578 |
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".