Will It Ever Be FAIR?
Bibliographic record
Abstract
Abstract A fundamental task of archaeology is to address challenging scientific questions related to the complexity of human societies. If we are to systematically understand the processes that affect human societies on multiple spatial and temporal scales, research leveraging existing archaeological data is essential. However, only a fraction of the data from archaeological projects are publicly findable or accessible, let alone interoperable or reusable. This is the case despite statements of disciplinary ethics, availability of capable technologies for data stewardship, publications providing guidance, and legal mandates. This article introduces the FAIR principles for data stewardship in North American archaeology, which state that data should be Findable, Accessible, Interoperable, and Reusable. We call for efforts to promote widespread adoption of the FAIR and CARE (Collective benefit, Authority to control, Responsibility, and Ethics) principles among professional organizations, publishers, data repositories, and researchers. We also call for adoption and implementation of requirements to adhere to these principles by governmental agencies, funding bodies, and other regulators of archaeological research. Ultimately, adoption of the FAIR principles in an ethical framework contributes to our understanding of our human experience and can lead to greater integration and reuse of research results, fostering increased partnerships between academia and industry.
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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 | 0.145 | 0.225 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.058 |
| Scholarly communication | 0.035 | 0.035 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".