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Record W3016455447 · doi:10.1080/01639374.2020.1751764

MARC to BIBFRAME: Converting the PCC to Linked Data

2020· article· en· W3016455447 on OpenAlexaff
Jacquie Samples, Ian Bigelow

Bibliographic record

VenueCataloging & Classification Quarterly · 2020
Typearticle
Languageen
FieldComputer Science
TopicLibrary Science and Information Systems
Canadian institutionsAlberta LibraryUniversity of Alberta
Fundersnot available
KeywordsCatalogingWork (physics)Computer scienceResource Description and AccessData conversionLibrary of congressLibrary scienceWorld Wide WebData scienceEngineeringOperating system

Abstract

fetched live from OpenAlex

The Program for Cooperative Cataloging (PCC) has formal relationships with the Library of Congress (LC), Share-VDE, and Linked Data for Production Phase 2 (LD4P2) for work on Bibliographic Framework (BIBFRAME), and PCC institutions have been very active in the exploration of MARC to BIBFRAME conversion processes. This article will review the involvement of PCC in the development of BIBFRAME and examine the work of LC, Share-VDE, and LD4P2 on MARC to BIBFRAME conversion. It will conclude with a discussion of areas for further exploration by the PCC leading up to the creation of PCC conversion specifications and PCC BIBFRAME data.

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)0.016
metaresearch head score (Gemma)0.070
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.990
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.070
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.008
Science and technology studies0.0020.003
Scholarly communication0.0100.010
Open science0.0020.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0280.019

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 head0.131
GPT teacher head0.290
Teacher spread0.159 · 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.

Study designNot applicable
Domainnot available
GenreMethods

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

Citations10
Published2020
Admission routes1
Has abstractyes

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