Bibliographic record
Abstract
The Elizabethan Gardens was designed in the mid-twentieth century as a living memorial for sixteenth-century English settlers who died on Roanoke Island, North Carolina. Today, it is a site where layers of mourning converge; for long-lost colonists and recently deceased relatives, absent Indigenous and Black people, and erosion-related land loss. Combining religious studies, multispecies studies, and literary studies of the Gothic, this article proposes the “ecological gothic” as a rubric for analyzing complex emotions and material relations in places, like the Gardens, that are defined by memory and ecological change. Its characteristics include an aesthetic of haunting, the melancholic romanticization of nature, and ruins or decayed infrastructure that seem to foreshadow collapse. In the Gardens, the ecological gothic shapes my discussion of human-plant relations, “unruly” infrastructure, mourning and spirit relations, set within a historical narrative that promoted American progress and White supremacy. Beyond the Gardens per se, I make the case for the ecological gothic as a framework through which scholars of religion might contribute to an emerging cross-disciplinary focus on how climate and ecological change is experienced, including in deeply emotional ways.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".