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Record W6902726264 · doi:10.7273/000006272

1980s WARC (Washington Archaeological Research Center): An Experiment in Communitarianism for Pacific Northwest Archaeology—And it did WARC (Work)

2022· article· en· W6902726264 on OpenAlexaboutno aff

Bibliographic record

VenueScholarWorks (Central Washington University) · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical and Cultural Archaeology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)State (computer science)State governmentFur trade

Abstract

fetched live from OpenAlex

The Washington Archaeological Research Center (WARC) was established in 1972 at Washington State University (WSU) to serve the six four-year universities and colleges, the state, and thus the public. WARC served as a clearinghouse for government contracts. Project assignments were made through a Scientific Committee of archaeology faculty. Dr. Richard D. Daugherty directed WARC until 1980, when the Administrative Board, and the Scientific Committee, redirected WARC away from contract coordination. Dr. Dale R. Croes was appointed Director, and WARC was tasked with maintaining the state’s site records and contract/research reports library. WARC was also directed to create the first computer database for survey projects and site records. WARC goals grew to include: facilitating research, training students, and conducting public outreach. WARC soon created an Advisory Council representing a broad collation of private contract archaeologists; professional archaeologists from Oregon, Idaho, and British Columbia, Canada; Native American Representatives; Federal/

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0110.005
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0140.003

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 head0.062
GPT teacher head0.309
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2022
Admission routes1
Has abstractyes

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