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Record W4312224962 · doi:10.17723/2327-9702-85.2.538

Exploring the Current State of North American Graduate Archival Education1

2022· article· en· W4312224962 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe American Archivist · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsLibrary and Archives Canada
Fundersnot available
KeywordsCurriculumListing (finance)Graduate educationLibrary scienceGraduate studentsDiversity (politics)Higher educationArchival scienceMedical educationPolitical scienceSociologyComputer sciencePedagogyMedicineBusinessLaw

Abstract

fetched live from OpenAlex

ABSTRACT This article analyzes 65 North American graduate archival education programs' course listings against current professional standards as crystallized in the 2016 Guidelines for a Graduate Program in Archival Studies (GPAS). The study addresses the following research questions: 1) What types of programs list graduate archival education courses?, 2) What types of courses do these graduate archival programs currently list?, 3) To what extent do archival programs' courses conform to GPAS?, and 4) What are the implications of a program's conforming or not conforming to GPAS? The authors' findings indicate an overriding tendency for graduate archival education programs to be hosted by LIS programs, especially under the auspices of iSchools. They identified a great diversity of graduate archival education programs and course listing combinations. Most important, they analyzed the archival curriculum coverage of 65 graduate archival programs to discern conformance with GPAS curriculum requirements. Although their findings may be used by programs for self-study, they also call into question the overall utility of GPAS and suggest the need for a more flexible approach.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.979
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.110
GPT teacher head0.251
Teacher spread0.141 · 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