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Students' E-Information Seeking Behaviour at KSCE, KIIT University, India

2013· article· en· W184883732 on OpenAlexaff
Dillip K. Swain, K. C. Panda

Bibliographic record

VenueE-LIS Repository (University of Naples Federico II) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsScience North
Fundersnot available
KeywordsReservationInterlibrary loanService (business)ConstructiveLoanThe InternetReservation systemInformation systemLibrary scienceBusinessPublic relationsComputer scienceWorld Wide WebSociologyMarketingPolitical science

Abstract

fetched live from OpenAlex

Libraries of engineering institutions are prominent information organizations that play vital role in catering to the information needs of the respective institutions. Students of engineering subjects need to update their knowledge through latest information in their respective field of interest which enables them to support their academic needs. How the information needs and seeking behavior of the students’ community is steered by the library professionals is indeed a big question. This paper intends to examine the overall information seeking behavior of students of school of computer engineering, KIIT University, India. The analysis reveals that students are absolutely satisfied with the loan of books (circulation) and internet service. However, respondents have a very poor opinion on reservation service and interlibrary loan. Moreover, the paper provides some constructive suggestions for the up- gradation systems and services of the university library.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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 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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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