Archaeological Practices and Societal Challenges
Bibliographic record
Abstract
Abstract Archaeology and archaeological work are tightly linked to contemporary societal challenges. Archaeology has much to contribute to the understanding, contextualising and working out of global challenges from migration to environmental change. In parallel to how archaeology impacts society, the society, societal changes, and challenges impact archaeology and its public mission of preserving and interpreting the physical and curating the informational archaeological record. Similarly, they impact archaeological practices, that is how archaeology is done in practice. This article draws attention to the need to comprehend what the increasing diversity and multiplicity of links between archaeological practices, knowledge work, and contemporary societal challenges implies for the understanding of how archaeology is achieved and archaeological knowledge is produced. The discussion is based on input collected from 50 members of the COST Action Archaeological Practices and Knowledge Work in the Digital Environment ( www.arkwork.eu ) who shared their views on how archaeology can contribute to solving contemporary societal challenges and what societal changes and challenges are likely to affect the field of archaeology during the next 5 years. In addition to a continuing need to increase the understanding of archaeological practices and their implications, distilling the outcomes of the state of the art into shared, validated, and actionable lessons learned applicable for societal benefit remains another major challenge.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.018 | 0.048 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".