Highlighting the Need for Collaboration Between Archaeologists and Information Professionals
Bibliographic record
Abstract
The discipline of archaeology intersects with information science as it deals directly with the information of human history and prehistory through material culture. This paper examines the link between the disciplines of information science and archaeology within the context of the archaeological curation crisis, which has been exacerbated by the lack of attention to the curation of archaeological collections. While the issue and solutions have been identified in the scholarly literature, few sources discuss collaboration between archaeologists and information professionals as a potential solution. This paper on conceptualizes archaeological artifacts as archival records and analyzes the issue through boundary object theory and the records continuum model. The purpose of this paper is to demonstrate the complementary nature of the two disciplines and subsequently highlight the importance of collaboration between archaeologists and information professionals, as both discipline have the potential to identify areas of need and establish best practices to resolve this curation crisis.
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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 | 0.066 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.029 | 0.019 |
| Scholarly communication | 0.035 | 0.040 |
| Open science | 0.004 | 0.041 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".