Is Archaeology a science? Insights and imperatives from 10,000 articles and a year of reproducibility reviews
Bibliographic record
Abstract
The status of archaeology as a science has been debated for decades and influences how we practice and teach archaeology. This study presents a novel bibliometric assessment of archaeology’s status relative to other fields using a hard/soft framework. It also presents a systematic review of computational reproducibility in published archaeological research. Reproducibility is a factor in the hardness/softness of a field because of its importance in establishing consensus. Analyzing nearly 10,000 articles, I identify trends in authorship, citation practices, and related metrics that position archaeology between the natural and social sciences. A survey of reproducibility reviews for the Journal of Archaeological Science reveals persistent challenges, including missing data, unspecified dependencies, and inadequate documentation. To address these issues, I recommend to authors basic practical steps such as standardized project organization and explicit dependency documentation. Strengthening reproducibility will enhance archaeology’s scientific rigor and ensure the verifiability of research findings. This study underscores the urgent need for cultural and technical shifts to establish reproducibility as a cornerstone of rigorous, accountable, and impactful archaeological science.
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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 | 0.185 | 0.535 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.062 | 0.049 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".