A Systems-Thinking Model of Data Management and Use in US Archaeology
Bibliographic record
Abstract
Overview Archaeology in the United States is caught in a “curation crisis” (Childs 1995; Childs and Warner 2019; Marquardt et al. 1982; SAA Advisory Committee on Curation 2003; Trimble and Marino 2003) and a “digital data crisis” (or “deluge”) more specifically (Bevan 2015; Clarke 2015; Kansa and Kansa 2021; Katsianis et al. 2022; Kersel 2015; McManamon et al. 2017:239–240; Rivers Cofield et al. 2024). Recent estimates suggest that, collectively, over 1.4 billion dollars are spent annually to support archaeological work that is mandated by federal law (SRI Foundation 2020). Although substantial efforts are underway to generate and provide mechanisms for managing, curating, and sharing the resultant digital data, we suggest that a critical step that has yet to be taken is to describe and visualize the components, connections, and causal dynamics of the US digital data system as it currently functions. Here, we specifically apply a “systems thinking” approach to produce such a high-level model of this system. We argue that understanding and visualizing this system will help us all “think bigger” (Heilen and Manney 2023); identify sources of knowledge, opportunities for critical analysis, collaboration, and capacity building; and increase much-needed archaeological digital literacy (Kansa and Kansa 2022). We conceptualize this as bringing “equilibrium” to the system, and in this article, we make several suggestions on how to bring this about. These insights can enable practitioners to better understand their roles in and contributions to the overall system and to evaluate efforts to improve data sharing, management, and curation practices not only within their organizations and departments but beyond.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".