The [ftaires!] to Remembrance: Language, Memory, and Visual Rhetoric in Chaucer's House of Fame and Danielewski's House of Leaves
Bibliographic record
Abstract
Geoffrey Chaucer's dream poem The House of Fame explores virtual technologies of memory and reading, which are similar to the themes explored in Danielewski's House of Leaves. "[ftaires!]", apart from referencing the anecdotal (and humorous) misspelling of "stairs" in House of Leaves, is one such linguistically and visually informed phenomenon that speaks directly to how we think about, and give remembrance to, our own digital and textual culture. This paper posits that graphic design, illustrations, and other textual cues (such as the [ftaires!] mispelling in House of Leaves] have a subtle yet powerful psychological influence on our reading and memory of texts. Paratextual or "secondary" features of a text such as its typography, font choice, line design, color scheme, and even minutiae like kerning collectively approximate an ur-character whose sole function is to educate the reader on how the book should be read. Other interests explored in this paper include: catalogs (as a form of archiving), houses of memory, ekphrasis, and unreliable/extra-diagetic narrators.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".