Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.117 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it