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Record W2515373269 · doi:10.3776/ncl.v74i1.725

Incorporating Branded Academic Library Programming to Promote and Showcase Campus Research and Artistic Performances

2016· article· en· W2515373269 on OpenAlexfundno aff
Christian Burris, Carolyn McCallum, Molly Keener

Bibliographic record

VenueNorth Carolina Libraries · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
FundersLeukemia and Lymphoma Society of Canada
KeywordsScholarshipAcademic librarySociologyDigital scholarshipLibrary sciencePublic relationsPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Academic libraries play a crucial role in the scholarship cycle. In addition to serving as a traditional study space, a university's community depends on their libraries to acquire and provide access to resources to support the research and instruction interests of faculty and students. In these ways, academic libraries support the cyclical nature of scholarship by collecting resources that are themselves both the tools necessary for generating future scholarship, and the outputs of past scholarship. This article focuses on two successful library-sponsored programs at the Z. Smith Reynolds Library at Wake Forest University. Its Library Lecture Series, which promotes faculty research and scholarship, and its Senior Showcase, which celebrates senior undergraduates’ research, have become a part of the cyclical scholarship cycle. Library-hosted programming that draws attention to the broad array of scholarship created on university campuses offers and provides opportunities for strengthening academic libraries’ relationships with their faculty, staff, student, and community users.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.027
Open science0.0000.000
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 head0.043
GPT teacher head0.326
Teacher spread0.283 · 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; a candidate call from one teacher head, not a consensus.

Study designObservational
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

Citations5
Published2016
Admission routes1
Has abstractyes

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