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Record W2772211041 · doi:10.25333/c3nk88

Research Information Management: Defining RIM and the Library’s Role

2017· article· en· W2772211041 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSwinburne Research Bank (Swinburne University of Technology) · 2017
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBusinessComputer scienceKnowledge managementProcess management

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Open science
Consensus categoriesScience and technology studies, Scholarly communication, Open science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.887
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0040.005
Scholarly communication0.0040.051
Open science0.0140.023
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.330
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it