Bibliographic record
Abstract
In the 10-year period between 1999 and 2009, the district of Barmer, located in the Marwar region of Rajasthan, India, experienced 7 years of rainfall deficits, as well as instances of excessive rainfall. This increased variability in rainfall patterns in an area largely covered by the Thar Desert ‘has exacerbated the region’s already precarious environmental and land conditions. This article is based on ethnographic research conducted in this part of India, which is impacted by the numerous social, economic, and environmental outcomes of successive extreme weather events. It discusses the transformation of the ecosystem of the Thar Desert by drawing the outlines of the recent environmental history and by exposing local farmers’ articulation of these changes. The meanings and subjectivities with which rural Rajasthan is endowed and which constitute farmers’ identity are also addressed through the examination of the cultural construction of place. The analysis reveals that people’s understanding of environmental change is intertwined with their broader worldview and their relationship with the elements that compose their immediate landscape. The author argues that a comprehensive understanding of the impact of climate change can only be reached by according more attention to the cultural dimensions of places.
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 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".