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Record W3116375017 · doi:10.5703/1288284317186

Dual-Campus Subject Librarians at University of Central Florida

2020· article· en· W3116375017 on OpenAlexaff
Barbara Tierney, Corinne Bishop

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsPurdue Pharma (Canada)
Fundersnot available
KeywordsSubject (documents)DowntownDual (grammatical number)Library scienceSociologyPoliticsPolitical sciencePublic relationsComputer scienceMedicineLaw

Abstract

fetched live from OpenAlex

A new dual-campus subject librarian program is being rolled out at the University of Central Florida (UCF) whereby several subject librarians divide their time between two campuses, the legacy main campus in East Orlando and the new Downtown Orlando Campus. As of Fall 2019, four UCF subject librarians regularly travel to the new Downtown Campus to provide library support for academic programs, faculty, and students who recently relocated to the new facility. Dual-campus subject librarians are also maintaining support services for their assigned academic programs that remain at the UCF Main Campus. This article provides information and reflections about how the dual-campus subject librarian model operates and how it impacts staff duties from two perspectives. The first perspective is from the UCF Social Sciences subject librarian, who supports graduate and undergraduate programs in The School of Public Administration and Public Affairs graduate programs at the Downtown Campus, as well as graduate and undergraduate programs in Politics, Security & International Affairs and Criminal Justice at the Main Campus. The second perspective is from the Main Campus Head of the Research and Information Services Department, who supervises the dual-campus subject librarians.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.231
Teacher spread0.203 · 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 designNot applicable
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

Citations0
Published2020
Admission routes1
Has abstractyes

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