LANDSCAPE IN ITS PLACE: THE IMAGINATION OF KASHMIR IN SANSKRIT AND BEYOND
Bibliographic record
Abstract
ABSTRACT Around the turn of the first millennium, new and experimental types of Sanskrit literature flourished in Kashmir. Poets like Bilhaṇa (eleventh century ce) and Maṅkha (first half of the twelfth century ce) embedded their works in the lived experience of medieval Kashmir, describing their home and family against the backdrop of the valley's mountains and cities. This regional self‐awareness reached a peak in the twelfth‐century poetic description of Kashmir, its kings, and its politics, the Rājataraṅgiṇī (River of Kings) written by Kalhaṇa. Kashmiri Sanskrit literature delighted in descriptions of the valley, yet this use of place and space has been until now little theorized. How is this sense of place constructed? What can the imagination of place in Kashmiri Sanskrit texts tell us about how the authors saw themselves in the world? This essay looks at these questions through a critical evaluation of Shonaleeka Kaul's monograph, The Making of Early Kashmir: Landscape and Identity in the Rajataraṅgiṇī. Kaul attempts to demarcate a specific Indic identity for Kashmir. Through a reading of the Rājataraṅgiṇī she posits a regionally coherent Kashmiriness that is nevertheless integrated into the wider Sanskrit cultural realm of the subcontinent. This essay both nuances and questions Kaul's broad claims while urging a careful reevaluation of Kalhaṇa's Rājataraṅginī and other literary representations of Kashmir's landscape. Here I argue that the descriptions of landscape must be contextualized within the broader rhetorical strategies of the text itself, and question Kaul's underlying claim of a Sanskrit identity that speaks itself through Kalhaṇa. By doing so I hope to highlight both the historical embeddedness and agency of Kashmiri poets in the eleventh and twelfth centuries.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.039 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".