Bibliographic record
Abstract
Aridity and Crisis: Cultural Narratives of Desertification in the US, reveals desertification as an understudied aspect of American cultural approaches to aridity and drought. While the scientific community has criticized desertification as an inaccurate representation of land degradation in arid and semi-arid regions (which, even in extreme drought conditions, are not transforming into deserts), the term remains popular in public discourse and in the context of international environmental governance, especially in the Sahel region of Africa. Accordingly, most scholarship on desertification has been geographically focused on the Sahel, whereas my project serves to locate desertification in the US cultural landscape. The long presence of these narratives in American cultural thought extends and clarifies the role of US science and policy in the establishment of desertification as it is known today: a popular narrative of global environmental crisis that is utilized to support development-based mitigation efforts. Aridity and Crisis engages in a chronological examination of changing cultural approaches to desertification in the US from the 1930s Dust Bowl to our contemporary moment using literary works, scientific and political texts, and policy documents. In my first chapter, I analyze the use of desertification discourse in the promotion of the New Deal’s agricultural polices within the works of economist Rexford G. Tugwell, photographer Arthur Rothstein, and filmmaker Pare Lorentz in the context of the Resettlement Administration. In my second chapter, I use Frank Herbert’s 1965 science fiction novel Dune to demonstrate how the postwar cultural fervor for sci-tech solutions and international development contributed to the dream of stopping desert growth everywhere. From here, I examine the role of US scientific expertise in the development of desertification ideology within the United Nations and international environmental governance. Finally, my fourth chapter brings this work to bear on the contemporary context of drought in the age of climate change. Using authors Paolo Bacigalupi and Claire Vaye Watkins, I argue that the experimental worlds of contemporary drought fiction offer a space wherein desertification as an idea may be re-imagined to explore the newfound scale and complex histories of drought and land degradation in a climate-changed world.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".