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Record W1984737324 · doi:10.1300/j201v05n01_05

The Role of Archival Authority Records in the Finding Aid System of the Archives of Ontario

2008· article· en· W1984737324 on OpenAlexaffabout
Steve Billinton

Bibliographic record

VenueJournal of Archival Organization · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsLibrary and Archives CanadaCanadiana.org
Fundersnot available
KeywordsRecords managementContext (archaeology)National archivesArchival scienceGovernment (linguistics)Authority controlPublic recordsComputer scienceLibrary scienceWorld Wide WebHistoryArchaeologyComputer security

Abstract

fetched live from OpenAlex

SUMMARY In the late 1990s the Archives of Ontario adopted a version of the Australian “series system” for the arrangement of Government of Ontario records. Series-based systems of arrangement and description provide an alternative means for archives to represent the relationships between a body of records, the functions and activities that generated them, and the organizational entities that carried out those functions. Integral to these systems is the use of archival authority records to represent the people and groups that create archival records. Implementing such a system does present challenges. It forces an archival repository to closely analyze the organizational context from which it receives records and to make systematic decisions about how this administrative environment will be represented in authority records so as to consistently represent the context of records creation. Moreover, once established, systems that document record creating bodies in archival authority records require a considerable ongoing resource commitment as new records are acquired and new context entities are described. This article outlines the characteristics of the Archives of Ontario's descriptive system and discusses some of the challenges presented in adopting such a system.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score0.221

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 studies0.0000.000
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 head0.018
GPT teacher head0.183
Teacher spread0.166 · 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 designTheoretical or conceptual
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

Citations4
Published2008
Admission routes2
Has abstractyes

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