Bibliographic record
Abstract
This chapter presents a dual perspective on the paratextual apparatus of a work of electronic literature, The Unknown: The Original Great American Hypertext Novel by William Gillespie, Scott Rettberg, Dirk Stratton, and Frank Marquardt. Approaches from literature studies and information science are combined to offer qualitative content analyses and close readings of the table of contents, titular apparatus, comments hidden in the source code, and other paratextual elements, in relation to the narrative. Findings indicate that the work's paratextual content presents inconsistencies and contradictions, both in terms of the use of the paratextual structure and of the information conveyed. The paratextual elements are analyzed through the lens of Gérard Genette's theory, as outlined in Paratexts: Thresholds of Interpretation, in order to gauge their role and efficiency as identifiers, organizational components, and information providers, as well as their literary effect. The value of the theory as an interdisciplinary tool is also discussed.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.035 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".