Resource-Type Descriptions For School Library Resources: Australian and New Zealand school library staff prefer user- friendly classifications to RDA and GMD vocabularies
Bibliographic record
Abstract
Descriptions of resource type assist students to discover resources. Under AACR2, bibliographic records contained the general material designation as a "first stop" in identifying typeness. Under RDA three controlled vocabularies describe content, media and carrier type. These went some way to address criticisms of GMD, however the language of the RDA terms is criticised as being unintuitive to users, and dispersing the description over three facets presents its own problems. In this context, libraries struggle to decide how to represent typeness to their end-users (Ou & Saxon, 2014). The Schools Catalogue Information Service (SCIS) provides high quality, consistent MARC records to schools internationally, including over 93% of Australian schools. SCIS ceased cataloguing of GMD in 2017 after four years of cataloguing records containing both GMD and RDA values. In 2016, SCIS surveyed 1212 Australian and New Zealand school library staff as a first stage in researching an alternative vocabulary incorporating user-friendly type (UFT) terminology. Results indicate that school library staff preferred UFTs to GMD and RDA terms and, where applicable, preferred terms where resource format is qualified in parentheses.
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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 | 0.023 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".