E-CURATORS - Pervasive Digital Curation Activities, Objects and Infrastructures in Archaeological Research and Communication: Process Modeling, Multiple-Case Studies, and Requirements Elicitation
Bibliographic record
Abstract
E-CURATORS is a mixed-methods, multiple-case study of archaeological projects employing novel digital technologies of curation “in the wild”. The units of inquiry are a number of case studies/archaeological activity sites, ranging from survey and excavation to digital illustration, long term digital preservation and access, and public communication and participatory curation of archaeological heritage. The project has the following objectives: (a) to produce a formal conceptual model and an evidence-based account of pervasive practices of digital curation in archaeological research and communication; (b) to identify and assess their implication on issues of epistemic and pragmatic importance for the future of the digital archaeological record; and, (c) to elicit requirements for digital infrastructures, as well as methods, procedures and best practice recommendations capable of addressing these issues. This report summarizes the background and motivation, scope, objectives, research questions, data and methods, originality, expected contribution and impact of the project.
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 | 0.027 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".