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Record W4387025353 · doi:10.18438/eblip30235

Practice of Infopreneurship by Librarians in Public University Libraries in South-South Nigeria

2023· article· en· W4387025353 on OpenAlexvenueno aff
Jerry Eyerinmene Friday, Oyinkepreye Sawyer-George

Bibliographic record

VenueEvidence Based Library and Information Practice · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsTest (biology)Library scienceMedical educationPopulationCollection developmentData collectionSociologyMedicineComputer scienceSocial science

Abstract

fetched live from OpenAlex

Objective – The aim of this study was to examine the practice of infopreneurship by librarians in public university libraries in South-South Nigeria. The study specifically intended to identify purposes of engaging in infopreneurship, methods of running infopreneurship, forms of infopreneurship practiced, benefits derived from practicing infopreneurship, and challenges encountered in practicing infopreneurship by the librarians. Methods – The population of the study comprised all 175 librarians in 13 public university libraries in South-South Nigeria, which were purposively chosen for the study. The study employed convenience sampling to engage 102 librarians in the university libraries, who were involved in one form of infopreneurship or another. The librarians were identified through preliminary investigation, observation, and interaction with the librarians by the researchers. The instrument for data collection was a self-designed online questionnaire titled, “Librarians’ Infopreneurship Practice Questionnaire (LIPQ).” The instrument was validated by two experts in the Department of Library and Information Science in Niger Delta University, Bayelsa State, Nigeria. Reliability test was not conducted on the instrument, based on the knowledge that a valid test tends to be reliable. The researchers distributed 128 digital copies of the draft of the validated questionnaire to the librarians through personal WhatsApp accounts of the librarians, WhatsApp groups of the various university libraries and WhatsApp groups of the different state chapters of the Nigerian Library Association to which the librarians belonged. Out of the 128 questionnaires administered, 102 were properly completed by the librarians and returned, producing a response rate of 97.69%. The data collected were analyzed using weighted mean and standard deviation. Results – The results from data analysis revealed that the librarians’ purposes of engaging in infopreneurship were to earn extra income, provide for post-retirement, meet unforeseen demands, and develop themselves. The librarians’ methods of running infopreneurship were leveraging, customizing, facilitating access to, and providing instant delivery of information. They used the following forms of infopreneurship: information brokerage, reprographic services, research-aid services, book vending and internet services. Finally, challenges faced by the librarians in practicing infopreneurship were lack of adequate finance, business infrastructure, technical skill, and high rate of presence of non-professional infopreneurs. Conclusion – The findings in this study demonstrate that librarians under study practice infopreneurship primarily for purposes of making money and self-sustenance. They achieve this by leveraging, customizing, facilitating access to and providing quick delivery of information. With these methods, the librarians engage in information brokerage, reprographic services, internet services, research-aid services and book vending. In return, these information professionals enjoy additional income, financial independence, accumulated knowledge, and enhanced sense of fulfillment. However, the practice of infopreneurship by the librarians is hindered by shortage of sufficient funds, technical know-how, business facilities and high rate of presence of unprofessional infopreneurs in the business.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0050.003
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.022
GPT teacher head0.263
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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