Prehistoric Mortuary Practices and the Constitution of Social Relationships: Implications of the First Radiocarbon Dates from Maski on the Occupational History of a South India “Type Site”
Bibliographic record
Abstract
In 1954, B K Thapar excavated the multicomponent site of Maski (Raichur District, Karnataka) to establish an archaeological sequence for the southern Deccan region of India. Thapar identified four major periods of occupation, now known as the Neolithic (3000–1200 BC), Iron Age (1200–300 BC), Early Historic (300 BC to AD 500), and the Medieval periods (AD 500–1600). Renewed research at the site by the Maski Archaeological Research Project (F.1/8/2009-EE) has investigated the development of social differences and inequalities in south Indian prehistory. This article reports the first ever radiocarbon assays from habitation and megalithic burial contexts in the vicinity of Maski. Accelerator mass spectrometry (AMS) dates of charcoal sampled from exposed occupational strata on Maski's Durgada Gudda hill and subsequent Bayesian analyses indicate that the site was extensively occupied during the 14th century AD, corroborating interpretations of numismatic and inscriptional materials. Associated artifacts with these 14C samples have significant implications for recognizing late Medieval period ceramics and occupation in the region. AMS assays of four charcoal samples from exposed megalithic burials just south of the Durgada Gudda hill, similar to those recognized by Thapar, indicate that burial practices commonly attributed to the Iron Age predate the period, and thus are not precise chronological markers. However, the results also suggest that megalithic burial practices became more labor intensive during the Iron Age, creating a cultural context for the generation of new forms of social affiliations and distinctions through differential participation in the production of commemorative 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".