Assessing Variability among Quartering Sites in Virginia
Bibliographic record
Abstract
The definition of what constitutes a Virginia slave quarter based on archaeological evidence is evolving. In the 1970s and 1980s, archaeologists developed an informal set of criteria that equated subfloor pits and the presence of "Africanisms" with structures occupied by enslaved people, and these criteria are still widely used. The accumulation of an archaeological and architectural data set of more than 170 Virginian quartering sites over the past 40 years has demonstrated that these sites vary across time and space, has underscored the problematic nature of site definition based on a checklist approach to ethnic or racial criteria, and has highlighted the challenges of inter-site comparison. We compare three quarters dating to the Revolutionary War and Post-Revolutionary periods. Our comparison underscores significant differences, as well as similarities, that existed between them and raises analytical challenges. Understanding variability and exploring alternative methods for site interpretation are important goals for the future. Employing analyses such as minimum vessel counts, assessments of richness, and abundance indices for artifacts, along with soil chemistry, ethnobotanical data, and landscape organization to understand historical landscapes, may prove to be more reliable methods of identifying quarters than relying on the presence or absence of certain features or artifact types.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".