Open Social Knowledge Creation and Library and Archival Metadata
Bibliographic record
Abstract
Standardization both reflects and facilitates the collaborative and networked approach to metadata creation within the fields of librarianship and archival studies. These standards—such as Resource Description and Access and Rules for Archival Description—and the theoretical frameworks they embody enable professionals to work more effectively together. Yet such guidelines also determine who is qualified to undertake the work of cataloging and processing in libraries and archives. Both fields are empathetic to facilitating user-generated metadata and have taken steps towards collaborating with their research communities (as illustrated, for example, by social tagging and folksonomies) but these initial experiments cannot yet be regarded as widely adopted and radically open and social. This paper explores the recent histories of descriptive work in libraries and archives and the challenges involved in departing from deeply established models of metadata creation.
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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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.015 | 0.087 |
| Scholarly communication | 0.026 | 0.028 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".