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The Relationship Between Library Use and Work Performance of Senior Non-Academic Staff in Private Universities in South Western Nigeria

2012· article· en· W1949406951 on OpenAlexvenueno aff
Ezinwanyi Madukoma, Sunday O. Popoola

Bibliographic record

VenueCanadian social science · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsAcademic libraryWork (physics)PsychologyInformation literacyMedical educationPublic relationsSociologyLibrary sciencePolitical sciencePedagogyEngineeringMedicineComputer science

Abstract

fetched live from OpenAlex

This study investigated the relationship between library use and work performance of senior non-academic staff in private universities in South Western Nigeria. Observation has shown that non-academic staff does not make regular use of the library as they occupy different positions that require decision making, unlike their counterparts, the academic staff who make regular use of the library for teaching, research, and other purposes. Through statistical analysis, it was found that there was no significant difference in work performance of the respondents by gender, secondly, there was no significant different in use of library of the respondents by universities, also, there was a significance in use of library of the respondents by gender, and there existed a significant relationship between library use and work performance of the respondents. It is therefore recommended that library managers should give user’s orientation and organize information literacy programme as well as tailor their information resources and services to the needs of the senior non-academic staff for their improved work performance. Key words: Library use; Work performance; Senior non-academic staff; Private universities; South Western Nigeria

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.020
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 head0.264
Teacher spread0.233 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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