Hidden in Plain Sight: The Unrecognized Contribution of the Survey of India in the Documentation of Ancient Settlements in Pakistan and India
Bibliographic record
Abstract
The earliest documentation of hundreds of ancient settlements in South Asia, including some of the most famous and significant sites, lies in largely unacknowledged subaltern hands. Operating during the British colonial period, teams employed by the Survey of India systematically mapped the colonial dominions and produced high-quality maps that depicted topography and land use across vast areas. Systematic analysis of these map sheets combined with ground-truthing is demonstrating that these teams documented thousands of mound features, and a significant number of these are (or sadly in many cases were) archaeological sites. Members of the original survey teams were for the most part not in a position to contribute their thoughts to the historical narrative, but the legacy of what they documented has long been hidden in plain sight. The collaborative Mapping Archaeological Heritage in South Asia (MAHSA) project is systematically documenting this archaeological heritage. Its work is demonstrating that the teams carrying out the Survey of India topographic surveys incidentally conducted the first systematic survey of archaeological sites in South Asia. This was potentially the world’s most extensive (albeit incidental) archaeological survey.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".