Improving Archaeologists’ Online Archive Experiences Through User-Centred Design
Bibliographic record
Abstract
Traditionally, the preservation of archaeological data has been limited by the cost of materials and the physical space required to store them, but for the last 20 years, increasing amounts of digital data have been generated and stored online. New techniques in digital photography and document scanning have dramatically increased the amount of data that can be retained in digital format, while at the same time reducing the physical cost of production and storage. Vast numbers of hand written notes, grey literature documents, images of assemblages, contexts, and artefacts have been made available online. However, accessing these repositories is not always straightforward. Superficial interaction design, sparsely populated metadata, and heterogeneous schemas may prevent users from working the data that they need within archaeological archives. In this article, we present the work of the Digging into Archaeological Data and Image Search Metadata project (DADAISM), a multidisciplinary project that draws together the work of researchers from the fields of archaeology, interaction design, image processing and text mining to create an interactive system that supports archaeologists in their tasks in online archives. By adopting a user-centred approach with techniques grounded in contextual design, we identified the phases of archaeologists work in online archives, which are distinctive to this user group. The insights from this work drove the design and evaluation of an interactive system that successfully integrates content-based image based retrieval and improved metadata searching to deliver a positive user experience when working with online archives.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".