Will you be my bf: forever? Analysing Techniques for Conversion to BIBFRAME
Bibliographic record
Abstract
The University of Alberta is actively trying to ramp up for Linked Data through local experimentation, research and partnerships with other institutions. Though BIBFRAME is still in development, several transformation tools have already been created, and with many libraries thinking about planning for moving to Linked Data it would seem timely to compare approaches to moving legacy MARC data to BIBFRAME. Setting aside the question of whether BIBFRAME should be the approach for libraries to move to Linked Data, this investigation aimed at comparing two tools for converting MARC to bf:2.0: A) LC MARC to BIBFRAME XSLT: An XSLT 1.0 application aimed at converting MARC to RDF/XML released in March 2017; and B) Casalini SHARE Virtual Discovery Environment: A project by Casalini Libri and @Cult to develop a Linked Data discovery environment, including a conversion tool for MARC to bf:2.0 RDF. Through the comparison and analysis of these transformation tools several topics will be explored: Comparison of underlying development models and performance of the tools; Comparison of data element conversion and impact for discovery; Impact of content standard on conversion efficacy (AACR vs. RDA); Implications for conversions for various formats (monographs, serials, et cetera); URI enrichment pre/post conversion; In house and vendor workflow implications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".