What We See, What We Don’t See: Data Governance, Archaeological Spatial Databases and the Rights of Indigenous Peoples in an Age of Big Data
Bibliographic record
Abstract
Archaeological spatial databases have the potential to enable deep insights into human history. These compilations of data are at the interface of data management and data visualization. Yet issues of data governance such as the nature, management, quality, ownership, security, and accessibility of archaeological spatial databases are under examined in archaeology, a situation that can affect data intensive methods and “big” data approaches. Data governance including laws and policies associated with data have bearing on archaeological practices which, in turn, can impact map visualizations and subsequent decision-making. With the growth of the geospatial web and Web 2.0 technologies, there are increasing opportunities for archaeologists and the general public to collect and engage with digital archaeological data. In Canada, greater numbers of specialists from different sectors (research and education, government, private companies) now accumulate, store, and process digital archaeological data. We draw from the OCAP® (ownership, control, access, possession) principles to shed light on data governance in archaeology, with a focus on archaeological spatial databases in Canadian archaeology. In this context, we draw attention to the rights of Indigenous peoples, the legal and policy issues associated with archaeological spatial databases, and a need for greater engagement with Indigenous data governance principles.
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.
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 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.017 | 0.075 |
| Scholarly communication | 0.023 | 0.029 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| 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".