Bibliographic record
Abstract
In Chaucer’s narratives, people think about the future, and typically they find it uncertain. Quelle surprise! you exclaim ironically, since narrative requires suspense in the steps between beginning and ending, or otherwise it would become the exposition of a static, allegorical, universal grid. The uncertain steps of narrative might only be those of characters within a story, whereas the omniscient narrator would know the plot and is beguiling the reader. For Chaucer, however, uncertainty extends to the narrator, and what is reached by the ending is only a hypothesis. There is also a choice of narrators. The beguilement of the reader in the suspense of a story becomes confrontation with something like a real problem of choosing from past to future. Where there is a real problem, there may be various trials of possible solutions. Each plan has steps taken in a distinctive pattern, and we learn distinct and ingenious ways of conceiving of what we may do in the course of time. Thus, among Chaucer’s other works, the loose gathering of Canterbury Tales rehearses tales of divergent strategy and scope for which contentious individual narrators were further invented. I will particularly consider "The Nun’s Priest’s Tale," but add some observations about Troilus and Criseyde.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".