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Record W2891191994 · doi:10.1080/00393630.2018.1476961

Preventive Conservation on Demand: Developing Tools and Learning Resources for the Next Generation of Collections Professionals

2018· article· en· W2891191994 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

VenueStudies in Conservation · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicConservation Techniques and Studies
Canadian institutionsGovernment of Canada
Fundersnot available
KeywordsPaceInformal learningKnowledge managementOn demandEngineeringBusinessComputer scienceMultimediaPolitical scienceGeography

Abstract

fetched live from OpenAlex

The modern learning environment is evolving at a rapid pace. Technology can help developers of preventive conservation tools and learning resources for collections professionals to increase their impact and reach. However, it is crucial to keep the needs of users, and gaps in skills and knowledge at the forefront. This article examines preventive conservation tools and resources developed by the Canadian Conservation Institute (CCI) and ICCROM (International Centre for the Study of the Preservation and Restoration of Cultural Property) over the past 30 years. In light of the results from a recent survey and research in the learning and development field, a set of orientations for future tool development are highlighted; these tools must be: need driven, user centered, emulating everyday experiences, social and informal, concise, mobile friendly, curated and open 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)0.001
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: Empirical
Teacher disagreement score0.598
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 studies0.0020.001
Scholarly communication0.0000.000
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 head0.344
GPT teacher head0.379
Teacher spread0.035 · 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