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Graduate Student Library Needs at Memorial University of Newfoundland: A Case Study

2023· article· en· W4383093182 on OpenAlexaffvenueabout
Victoria A.J. Kavanagh, Natallia Barykina

Bibliographic record

VenuePartnership The Canadian Journal of Library and Information Practice and Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPromotion (chess)Graduate studentsWork (physics)Medical educationTheme (computing)Academic libraryLibrary sciencePsychologyPolitical scienceEngineeringMedicineComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Libraries are important structures for graduate students in research intensive universities, such as Memorial University of Newfoundland (MUN). Given the complex needs of those students, it is crucial to have an updated glimpse into what works and does not work for students. The objectives of this survey were to investigate graduate students’ awareness and use of library services and resources at MUN and to explore how important and adequate existing services and resources seemed to MUN graduate students. Fortunately, the survey findings showed favourably in terms of user satisfaction, with many of the library services and resources that were rated as very important also being rated as very satisfactory. Other findings indicated that respondents placed increased importance on access to comprehensive collections of E-resources, which is not surprising given the conditions of the COVID-19 pandemic. Also found was an increased need for writing resources and literature search strategies. Overall, a recurring theme for improvement was an increased promotion of our services and resources so that we can better reach our students. The insights gained from the survey will help us target this area of improvement and direct future development of graduate student-focused services and resources.

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)0.004
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.487
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.098
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 head0.113
GPT teacher head0.380
Teacher spread0.267 · 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 designQualitative
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
Published2023
Admission routes3
Has abstractyes

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