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Record W2014347812 · doi:10.1108/02640470710741331

Building interoperable Canadian architecture collections: initial metadata assessment

2007· article· en· W2014347812 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Electronic Library · 2007
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsMcGill University
Fundersnot available
KeywordsMetadataInteroperabilityArchitectureComputer scienceCollections managementNormalization (sociology)World Wide WebInformation retrievalDatabaseArchaeologyGeography

Abstract

fetched live from OpenAlex

Purpose The purpose of this research is to assess the current descriptions of architecture collections housed at the McGill University Library in preparation for building an interoperable metadata and search interface for Canadian architecture collections. Design/methodology/approach The names and frequencies of tables and fields of 11 architecture databases were analyzed and summarized into the most commonly used groups. In addition, typologies of buildings by purpose of construction were presented as subject headings. Findings Current metadata schemes are diverse and heterogeneous across the 11 databases. Research limitations/implications This study is at the pilot stage and is limited to Canadian architecture collections at McGill University. The observations provide insights into metadata normalization that can be used as a basis for building architecture collections or image collections. Originality/value This is the first metadata assessment of architecture collections for the purpose of building a single uniform access.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.913
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.226
Teacher spread0.210 · 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