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Record W2889497287 · doi:10.11575/prism/29230

The Role of Next Generation Libraries in Enhancing Multidisciplinary Research

2016· article· en· W2889497287 on OpenAlexaboutno aff
Tom Hickerson, Joan K. Lippincott, Kathryn Ruddock, Shawna Sadler

Bibliographic record

VenueOpen MIND · 2016
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsnot available
Fundersnot available
KeywordsMultidisciplinary approachComputer scienceSociologySocial science

Abstract

fetched live from OpenAlex

How well are research libraries positioned to meet the needs of today’s multidisciplinary research? What are the common needs across disciplines conducting such research? These are the questions the University of Calgary Libraries and Cultural Resources sought to answer through focused discussions among researchers extending over three days in the fall of 2015. With funding from The Andrew W. Mellon Foundation, we hosted workshops with researchers from three multidisciplinary research clusters of strategic priority to the University of Calgary – Arctic Studies, Smart Cities and Visual Analytics. External facilitators managed the various sessions and three disciplinary experts from other Canadian universities (Toronto, Carleton and Queens) contributed in broadening the scope of the inquiry. Library staff and representatives of the University’s Research Services Office acted principally as observers, but contributed as needed in identify existing research infrastructure capacities. In this project briefing, we will discuss the planning and conducting of these workshops, report on the common research support needs and themes, examine the implications for 21st century libraries in planning services and technologies and explore their role in the development of research platforms.

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.

How this classification was reachedexpand

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0030.032
Open science0.0040.005
Research integrity0.0000.000
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 head0.312
GPT teacher head0.444
Teacher spread0.132 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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