Retaining Roots While Hard Pruning Data: Context and Collaboration in Digitisation and Data Modelling in South Asia
Bibliographic record
Abstract
There are large numbers of published (and unpublished) archaeological sites and monuments in South Asia, but much of the available documentation is not available in a digital format, and paper publication remains the default. Information about archaeological heritage varies in detail, and there is considerable variation in how archaeological sites are documented. This paper explores the work and challenges of the Mapping Archaeological Heritage in South Asia (MAHSA) project in digitising, modelling, re-interpreting, linking, and re-using non-digital archaeological data into a structured and standardised digital format using a common and unique controlled vocabulary, so that it may become findable, accessible, interoperable, and reusable data. Working with partners in South Asia, the MAHSA project is compiling existing and published data (from published documents, reports, and surveys) to be published in an Open Access database (Arches platform), which can be used for research and preservation. New and previously undocumented sites identified through the analysis of historic maps, remote sensing, automated site detection methods, and field documentation will also follow the same data standards and recording methodology. Retaining context while delivering ‘cleaned’ usable data is a key challenge to this type of data modelling, and the meanings of the original source and context are inevitably subject to re-interpretation during this process. The project is working collaboratively between South Asian and international stakeholders, each bringing their own expectations of data use, management, and accessibility, in addition to navigating the post-colonial space in which the project operates. To ensure usability, different use cases scenarios have been identified and are being developed with collaborators and database end-users. We argue that through linking archaeological data to their original sources and following best practices in modelling techniques, we can remain faithful to the original context while incorporating any change and enhancements where data needs to be verified and updated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".