Anthropocene and the Gothic: An Interview with Justin Edwards
Bibliographic record
Abstract
Justin Edwards is a professor in the Division of Literature and Languages at the University of Stirling. Previously Chair of English at the University of Surrey and professor and head of English at Bangor University, he was elected by-fellow of Churchill College, Cambridge in 2005. Between 1995 and 2005, he taught at the University of Montreal and the University of Copenhagen, where he was appointed as an associate professor in 2002. He holds an Affiliate Professorship in U.S. Literature at the University of Copenhagen and in 2016-2017 he was a Fulbright scholar at Elon University, North Carolina. He is also a member of the Peer Review College for the Arts and Humanities Research Council (AHRC) and a Trustee of the Modern Humanities Research Association (MHRA). Justin’s contribution to the study of Gothic literature started with Gothic Passages: Racial Ambiguity and the American Gothic, which examines the development of U.S. Gothic literature alongside 19th-century discourses of passing and racial ambiguity. In Gothic Canada: Reading the Spectre of a National Literature, he continued in the area by examining how collective stories about national identity and belonging tend to be haunted by artifice.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| 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".