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Record W4392937377 · doi:10.18438/eblip30375

A Survey of Knowledge and Use of Academic Library Services at a Pseudo-Satellite Location

2024· article· en· W4392937377 on OpenAlexaffvenue
Jason Lee, R. A. HEAD, Courtney Vienneau, Jasmine Hoover, Martin Chandler

Bibliographic record

VenueEvidence Based Library and Information Practice · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsGovernment of Newfoundland and LabradorCape Breton University
Fundersnot available
KeywordsOutreachSpace (punctuation)PopulationAcademic libraryLibrary scienceWorld Wide WebComputer scienceMedical educationSociologyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Objective – Following a rapid increase in student population over a five-year period, Cape Breton University leased additional teaching space from a nearby cinema chain but did not account for students’ library needs. The local nature of the venue, combined with issues in transit to the main campus, created “local-distance” students. These students were surveyed on awareness and use of library resources and services to inform future services. Methods – Students whose classes were primarily located at the cinema chain were engaged in an anonymous survey regarding their knowledge and use of library services. These data were then analyzed for common themes and recommendations. Results – There were notable gaps in student knowledge and use of library resources and services, perhaps owing to the primary source of information regarding these – namely, friends, professors, and the website. The need for further outreach and onsite library workers was highlighted, as was the importance of library as space. Conclusion – While the library handled the new venue as well as possible, it is crucial for administrators involved in change management to remember that student learning involves more than individuals in a classroom seat.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.723
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 head0.032
GPT teacher head0.313
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

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

Citations2
Published2024
Admission routes2
Has abstractyes

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