Emerging convergence? Thoughts on museums, archives, libraries, and professional training
Bibliographic record
Abstract
While ‘convergence’ has been a topic of much discussion in the museum, archive, and library communities, the emerging similarities between these three types of cultural heritage institutions – most apparent in their on-line activities – are not yet evident in the education of professionals who work in them. Curriculum models still support traditional definitions of the roles, functions, and audiences of archives, libraries, and museums. Professional practice can evolve in the context provided by digital heritage and digital curation, and respond in a manner that supports common goals across institution types. New inter-disciplinary foci for professional training can provide skills needed across the sector, while respecting the distinct histories, cultural roles, and responsibilities of libraries, archives, and museums.
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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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.017 | 0.065 |
| Scholarly communication | 0.027 | 0.048 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 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".