‘Sublime Oilscapes’: Literary Depictions of Landscapes Transformed by the Oil Industry
Bibliographic record
Abstract
Abstract Literary reactions to the transformation of landscape by modern technology foreground the fragility of the planet while at the same time suggesting notions of immensity and inspiring awe. Oil mining, in particular, threatens and destroys essential mega-biotopes, as for example two of the biggest wetlands on earth, the Athabasca Tar Sands in Canada’s northern Alberta and the Niger Delta in southern Nigeria. While we are flooded, daily, by media reports on environmental damage and by scientifically based scenarios of future catastrophes, it is literature with its specifically ambiguous and multidimensional make-up, which proves to be an ideal medium to foreground the ambivalence of twenty-first century societies regarding their attitude towards a radically modified natural environment. The double aesthetics of the sublime, in particular, proves to be a congenial creative (and critical) approach to these fear- and awe-inspiring landscapes, which have been forged and shaped by technology and industry. In my essay I want to show how twenty-first century Canadian and Nigerian writers have responded to the effects of oil mining in their respective countries by drawing on notions of the sublime as they came to be articulated by Edmund Burke in the eighteenth century and have been taken up by scholars in the twentieth and twenty-first centuries. Through their narrative and poetic ‘sublime oilscapes’ these authors effectively foreground the problems inherent in the split attitude of contemporary societies.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".