Information Resource Description: Creating and Managing Metadata. Second Edition. (book review)
Bibliographic record
Abstract
Resource Description provides a broad yet detailed sweep of metadata best practices and applications.It is clearly organized and steeped with references and links to an incredibly robust amount of resources.I am confident in proclaiming it a bible for any information professional who is new to metadata application or who wants to improve their understanding of the landscape and trends in the complex world of seemingly infinite metadata elements.While many library associations have seminal works that are discipline-specific, such as the Music Library Association's guide for sound and audiovisual archives (Jenn Riley's Glossary of Metadata Standards, for example), Hider's sweeping text provides a foundational resource for those new to this aspect of librarianship and one that can be applied to many subject areas.It expands on the discipline-specific texts by incorporating an element of quality improvement and systems management for which librarian expertise is required and able to be widely applied.
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 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.038 |
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".