Accelerating the use of digital object identifiers (DOIs) in academic libraries
Bibliographic record
Abstract
This paper explores the use of digital object identifiers (DOIs) in Canadian academic libraries. We analysed survey responses from 40 Canadian research organizations in order to understand the variables that accelerate or hamper the adoption of DOIs for digital research collections. Barriers to adoption include issues relating to technical barriers, lack of plugin integration, lack of dedicated technical staff, and the absence of institution-wide policy. Accelerants include the use of shared national research infrastructure, national funding to help reduce costs to libraries, and national consortia as technical liaisons and educators. Three priority areas for education include: guidance to help librarians decide when to apply a DOI to a resource; examples of metadata mappings for archival holdings and non-traditional formats; and the urgent need for support in maintenance planning for sustaining DOIs over the very long term.
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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 | 0.129 | 0.352 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.027 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".