Retrospective Prophecy and Medieval English Authorship, by Kimberly Fonzo
Bibliographic record
Abstract
Political prophecy, once a relatively under-explored field, has garnered significant interest since Lesley Coote’s Prophecy and Public Affairs in Later Medieval England (2000) and is now an established field for literary scholars and historians alike. Kimberly Fonzo’s monograph represents a valuable contribution to the field, for it explores the relationship between prophetic engagement and authorial identity in Ricardian English literature. In the introduction, Fonzo outlines the aim of her book and defines what she terms retrospective prophecies as ‘predictions that readers have ascribed to authors ex post facto’ (p. 4). Such supposed prophecies, she argues, oversimplify the causes of the event they supposedly predicted and lead readers to date the works prior to the past events they apparently predicted. The first chapter discusses the diverse approaches to prophecy by Ricardian writers, notably the use of the prophetic voices of Merlin and Sybil. She posits that many of these writers’ uses and treatment of political prophecy were a result of political pressures, such as the need to encourage support for the French military campaigns or to express frustration with their cessation. Following this chapter, Fonzo focuses on specific authors (William Langland, John Gower and Geoffrey Chaucer), and she suggests that these writers and their works have long been misunderstood as prophetic. She traces the history of these misunderstandings, debunks such retrospective prophecies, and analyses those prophetic approaches which the authors did employ.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".