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Record W4385729258 · doi:10.1017/aap.2023.17

Embedding Librarians in Archaeological Field Schools

2023· article· en· W4385729258 on OpenAlexaff
Gabriel Hrynick, Arthur W. Anderson, Erik C. Moore, Mike Meade

Bibliographic record

VenueAdvances in Archaeological Practice · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsField (mathematics)PublishingArchaeologyField researchExperiential learningSociologyLibrary scienceHistoryPedagogyPolitical scienceSocial scienceComputer science

Abstract

fetched live from OpenAlex

Abstract Participating in an archaeological field school is one of the only educational experiences that nearly all professional archaeologists have during their training. As a result, field schools are uniquely suited to provide experiential education in emerging skills that all archaeologists will need, such as information and data literacies at all stages of the contemporary research and publishing cycle. The “embedded” librarian program in the University of New Brunswick's Downeast Maine Coastal Archaeology Field School is an effective means to deploy that focused expertise to help students better understand the relationship between fieldwork, data, and dissemination. At the same time, being in the field provides librarians with the knowledge to respond more effectively to the complex data management and research needs of archaeologists. We encourage large research projects to consider librarians as specialist members of the research team.

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)0.058
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.969
Threshold uncertainty score0.306

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0580.042
Meta-epidemiology (narrow)0.0010.003
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.008
Science and technology studies0.0220.012
Scholarly communication0.0310.023
Open science0.0060.036
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0540.024

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 head0.038
GPT teacher head0.302
Teacher spread0.264 · 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.

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

Citations6
Published2023
Admission routes1
Has abstractyes

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