Archaeofaunal Remains, Geography, and the Investigation of Cultural Keystone Places
Bibliographic record
Abstract
Cultural Keystone Places (CKPs) are areas on the landscape crucial to individual and group identities, especially descendant communities. As such, they are often significant components of Indigenous land claims and cultural continuity. CKPs commonly have deep temporal roots and unclear spatial boundaries, and archaeological investigation is often relied upon to define them. However, relying on archaeological prospection and data to define a CKP can be problematic. The discovery of archaeological material and, by extension, a CKP is a probabilistic endeavor, often constrained by preservation conditions and sampling strategies. While many archaeologists understand that the material record will always be incomplete and that the absence of archaeological materials does not indicate the absence of a CKP, this view is juxtaposed with comparatively simple legal or regulatory understandings of CKPs as areas exclusively defined by either the presence or absence of archaeological materials in places such as British Columbia, Canada, which we discuss in this paper. To frame that discussion, we turn to the archaeological record from a different region; we use a large multisite database from southwestern Colorado—created and curated by the Crow Canyon Archaeological Center—to illustrate the variability in the quality of the archaeological record across the landscape. By modeling the fragmentation and sample size of animal remains, we demonstrate how even systematically collected archaeological data can still lead to knowledge gaps, potentially resulting in a false negative for the presence of a CKP. We therefore urge regulatory agencies to more thoroughly consider the sampling strategies and preservation conditions of remains related to the investigation of CKPs and to highlight the value of using robust archaeological databases to support Indigenous land rights and the identification and protection of CKPs.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.023 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".