Bibliographic record
Abstract
This chapter focuses on paratext that is typically considered extradiegetic, and as such it is often experienced as optional. Readers can say that they have read <italic>Middlemarch</italic> without reading every single epigraph; they can certainly ignore footnotes at will, especially if they were written by an actual editor. Indeed, fictional paratexts may be more powerful than scholarly ones. Autographic notes are perhaps more significant for most literary readers than are allographic additions. The chapter also consider paratexts—both epigraphs and footnotes—in <italic>Middlemarch</italic>, Catherine Parr Traill's <italic>The Canadian Crusoe</italic>s, Rudyard Kipling's <italic>Kim</italic>, and José Rizal's <italic>Noli Me Tangere</italic> as a way to examine bibliographic metalepsis: the infinite library that lurks in the margins of the text, and sometimes breaks through them because of the force of an allusion, the impact of information, or the oddness of bits of text attached but also detachable from the “main” text we read.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".