Dreaming of authors, authoring dreams: Literary authorship in the framed first-person allegories of John Skelton, William Dunbar, Stephen Hawes, and Gavin Douglas
Bibliographic record
Abstract
This thesis investigates the distinctive conceptions of literary authorship of John Skelton, William Dunbar, Stephen Hawes, and Gavin Douglas by means of close and comparative readings of their utilisation of a particular form and mode: framed first-person allegory. Each of the poets examined makes claims for the textual authority of their writings—that is, those qualities which make a text worth reading and reproducing. For most, those claims are based on the attribution of the work to a human author, whose skill, learning, and morality add value to the text. Skelton’s strategy for authorial self-promotion of this kind is to represent himself as an author within an allegorical dream poem, for which Chaucer provides the most important models in English. Yet for others, framed first-person allegory functions as a largely depersonalised form and mode, a compilation and negotiation of texts and tradition, or sometimes as a way to represent the kind of author that the poet is not. This thesis asks: what kind of authors are imagined in the framed first-person allegories of late fifteenth- and early sixteenth-century English and Scottish poets?; but also, when is self-representation-as-author not considered to be the most effective strategy for authorial self-promotion, and what are the alternatives? Responses to changing systems of patronage and publication, cognizance of certain humanist ideals, and intersection of what have been understood as ‘medieval’ and ‘modern’ attitudes to poetic predecessors, especially Chaucer, are considered in the works of four poets who have too often been consigned to the footnotes of larger diachronic surveys. The picture that emerges is of an interconnected but multifaceted array of literary authorships, responsive to, but not determined by, contemporary political, social, and technological factors, and which complicate accounts of ‘the emergence of the English author’ in late medieval and early modern England and Scotland.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".