Quality Control Issues in Outsourcing Cataloging in United States and Canadian Academic Libraries
Bibliographic record
Abstract
This study was conducted to investigate the quality control (QC) issues in cataloging outsourcing programs implemented in U.S. and Canadian academic libraries. Most libraries provided the outsourcing vendors with detailed cataloging and/or processing specifications before the outsourcing programs started. They have set up QC procedures as an integral part of their outsourcing operations. In most cases, both librarian-catalogers and senior library assistants/technicians were involved in the QC programs. The error rates reported were low and the majority of bibliographic records provided by the vendors were either LC/OCLC records or records compatible with the Core-Level Standard recommended by the Cooperative Cataloging Council's Task Group on Standards. A large majority of these libraries were satisfied with the services provided by the outsourcing vendors. Based on the definition of quality of cataloging as a combination of accuracy, consistency, adequacy of access points, and timeliness, most libraries reported that the quality of their library's cataloging was not affected by the outsourcing programs.
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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 | 0.026 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.022 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".