From Pilot Project to Three FTE: UBC's decentralized repository staffing model
Bibliographic record
Abstract
In 2011, cIRcle, the University of British Columbia’s open access digital repository, formally accepted a decentralized repository staffing model following a full day planning retreat with key Library stakeholders. Six years later staff has grown to include two FTE Digital Repository Librarians, one FTE support staff, as well as secured metadata review commitments from a Librarian and two cataloguers in Technical Services in addition to regular deposit support from student employees. With increased capacity has come robust and well-documented metadata standards that support interoperability; automated content ingest streams; improved permissions review support to meet growth in faculty requests; streamlined workflows; and strategic content recruitment efforts. This presentation will review how cIRcle evolved from pilot project to top ranking Canadian IR by employing key strategies to advocate for staff allocations that are adaptable to a range of academic institutions. Both successes and failures of cIRcle’s workflows and initiatives using the decentralized model will highlight ongoing challenges of and insights into supporting existing and emerging trends in repository services.
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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 | 0.025 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".