Curating Archaeological Provenience Data Across Excavation Recording Formats
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Archaeological excavations today generate extensive datasets across survey, excavation, and analysis activities, especially when they are conducted in collaborative structures such as field schools. Working across such activities, data archivists contribute to the goals and research outcomes of the dig by establishing data practices that are participatory and educational (two pillars of data literacy) as they permanently record information about the archaeological results. At the Venus Pompeiana Project (VPP), a collaborative archaeological investigation of the Sanctuary of Venus in Pompeii, both provenance and provenience data are recorded into a database at the trenches’ edge, which optimises the accuracy of the data by allowing direct input and review by the data creators and archaeological site experts. When legacy data about work conducted decades or even centuries earlier are brought into the data picture, scholars stand to gain a deeper understanding of the geographic locations of key interest over time. Yet, the integration of analogue legacy and digital archival datasets is collaborative and longitudinal work. In this paper, we bring together experiential reflections on data archiving conducted at both the excavation site and in the physical archives of the Pompeii Archaeological Park. We then provide an integrative analysis of the outcomes of such data curation, highlighting what each data archiving contributor “discovered” about the site as a whole or a specific artefact, feature, or data category. Our findings contribute deeper insights into what data archiving and format-specific curation activities are most effective for learning experiences, archaeological scholarship, and professional practices.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it