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Record W6963014849 · doi:10.17617/3.nann0g

Staccioli Digital Archive metadata

2024· dataset· en· W6963014849 on OpenAlexaff

Bibliographic record

VenueMax Planck Digital Library · 2024
Typedataset
Languageen
Field
Topic
Canadian institutionsMcGill University
Fundersnot available
KeywordsMetadataRDFExhibitionLinked dataDigital curationData archiveDigital data

Abstract

fetched live from OpenAlex

This dataset contains the entire metadata export of the Staccioli Digital Archive, a contemporary art catalogue that documents the life and works of Italian contemporary sculptor Mauro Staccioli. It does not contain scanned images nor direct references thereto. The dataset is available as Linked Open Data and formatted in RDF using CIDOC-CRM v7 as its schema. The data are aligned with the GND authority file, the ULAN and AAT thesauri, and Wikidata. The catalogue and virtual exhibition are available at https://staccioli.biblhertz.it, whereas the live dataset can be queried and explored at http://data.biblhertz.it under the same terms as this data export.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.068
Threshold uncertainty score0.228

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.013
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0680.135

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 head0.012
GPT teacher head0.214
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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