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Record W68199531 · doi:10.22191/neha/vol38/iss1/1

Assessing Variability among Quartering Sites in Virginia

2009· article· en· W68199531 on OpenAlexaboutno aff
Barbara J. Heath, Eleanor Breen

Bibliographic record

VenueNortheast Historical Archaeology · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical and Cultural Archaeology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArtifact (error)ChecklistArchaeologyQuarter (Canadian coin)Interpretation (philosophy)HistorySet (abstract data type)Historic siteGeographyGenealogyComputer scienceBiology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.296
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2009
Admission routes1
Has abstractyes

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