Bibliographic record
Abstract
The Postsecular Restoration undertakes an exploration of a phenomenon that is often remarked upon but frequently disavowed: that many of the writers of the Restoration period who are considered innovators in the literary field – in particular Margaret Cavendish, Aphra Behn, and John Dryden – were politically conservative, in that they supported monarchy, they were Catholic, they were intolerant, or they were antidemocratic. This book makes the case for the synergistic relationship between a nascent postsecular worldview – one based not on clinging to tradition but in a thoughtful response to emergent liberal secular ideals and practices – and the emergence of the modern sphere of literature, in which conservative writers play a prominent role. Thus, it addresses a critical blind spot: The conservative political orientation of these writers has typically been treated as separate from their literary contributions, leveraged against their literary contributions, explained away as a function of their historical conditions, or ignored altogether. One reason for this may be that literary scholars tend to identify with liberal or progressive politics, and, as a result, our means of seeing connections between literature and politics have tended to obscure questions of how conservative political orientation and literary innovations relate.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.508 | 0.293 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".