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Record W4288060708 · doi:10.18357/kula.232

Modelling Linked Data for Conservation

2022· article· en· W4288060708 on OpenAlexvenueno aff
Ryan Lieu, Alberto Campagnolo

Bibliographic record

VenueKULA knowledge creation dissemination and preservation studies · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicConservation Techniques and Studies
Canadian institutionsnot available
FundersArts and Humanities Research Council
KeywordsDocumentationMetadataComputer scienceMateriality (auditing)Scope (computer science)Event (particle physics)Object (grammar)Data scienceDescriptive statisticsInformation retrievalWorld Wide WebArtificial intelligence

Abstract

fetched live from OpenAlex

Conservation documentation serves an invaluable role in the history of cultural property, and conservators are bound by professional ethics to maintain accurate, clear, and permanent documentation about their work. Though many well-documented schemata exist for describing the holdings of memory organizations, none are designed to capture conservation documentation data in a semantically meaningful way. Conservation data often includes deeply detailed observations about the physical structure, materiality, and condition state of an object and how these characteristics change over time. When included with descriptive catalog metadata, these conservation data points typically manifest in seldom-used fields as free-text notes written with inconsistently applied standards and uncontrolled vocabularies. Beyond the traditional scope of descriptive metadata, conservation treatment documentation includes event-oriented data that captures a sequence of steps taken by the conservator, the addition and removal of material, and cause-and-effect relationships between observed conditions and treatment decisions made by a conservator. In 2020, the Linked Conservation Data Consortium conducted a pilot project to transform unstructured conservation data into linked data. Participants examined potential models in the library field and ultimately chose to conform to the Comité International pour la Documentation (CIDOC) Conceptual Reference Model (CRM) for its accommodation of event-oriented data and detailed descriptive attribution. Project technologists worked with real report data from four institutions to create XML data models and map newly structured data to the CRM. The pilot group then imported CRM-modelled datasets into a discovery environment, developed queries to reconcile the divergent datasets, and created knowledge maps and charts in response to a small set of predetermined research questions. Feedback from conservators attending workshop activities revealed a shared need for conservation data standards and guidelines for those developing documentation templates and databases. Project outcomes signalled the necessity of further developing conservation vocabularies and ontologies to link datasets between institutions and from adjacent domains.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.552
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.268
GPT teacher head0.397
Teacher spread0.130 · 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.

Study designNot applicable
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

Citations1
Published2022
Admission routes1
Has abstractyes

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