Research Information Management: Defining RIM and the Library’s Role
Bibliographic record
Abstract
Research information management (RIM) is the aggregation, curation, and utilization of information about research and is emerging as an area of increasing interest and relevance in many university libraries. RIM intersects with many aspects of traditional library services in discovery, acquisition, dissemination, and analysis of scholarly activities, and does so through the nexus with institutional data systems, faculty workflows, and institutional partners. RIM adoption offers libraries new opportunities to support institutional and researcher goals.In this paper prepared by Rebecca Bryant, OCLC Research Senior Program Officer, and a working group of librarians representing OCLC Research Library Partnership institutions, learn more about what RIM is, what is driving RIM adoption, and the library's role in RIM.The publication is intended to help libraries and other institutional stakeholders understand developing research information management practices—and particularly the value add that libraries can offer in a complex ecosystem.This work is part of a suite of publications and resources around RIM practices.
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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 | 0.124 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.023 |
| Science and technology studies | 0.013 | 0.051 |
| Scholarly communication | 0.073 | 0.082 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".