Speaking Stones: Oral Tradition as Provenance for the Memorial Stelae in Gujarat
Bibliographic record
Abstract
Anthropological fieldwork in rural settlements on the west coast of India has unraveled the close connection between lived experiences, spaces and objects. These “inalienable possessions”, in the words of Annette Weiner, help reconstruct the past through the supplementation of oral traditions. Following this vein, the paper attempts to mesh together the material culture and oral histories to establish the provenance for the plethora of memorials in the state of Gujarat. A series of oral narratives collected in Western India since 2014 has highlighted the role of medieval memorial stelae that commemorate the deceased heroes of war and their wives and companions. This paper creates a niche for the Gujarati oral tradition as provenance for the continued veneration of these memorials. Field observations from 2014–2016 and notes from research in Gujarat from 1985 onwards enabled the study of patterns in the oral preservation of literature. A systematic documentation of the existing stelae and associated oral traditions has informed the views in this paper. The paper speaks to all levels of interaction and the making of an identity for the memorial stones that are unique to the state of Gujarat. A case for the inclusion of such rich material in museum displays is made in connection with this case study of the memorial stelae in Gujarat.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".