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Record W280223270 · doi:10.4275/kslis.2005.39.4.071

A Study on Core Values of University Libraries through Missions and Visions

2005· article· en· W280223270 on OpenAlexaboutno aff
Yoon-Hee Cho

Bibliographic record

VenueJournal of the Korean Society for Library and Information Science · 2005
Typearticle
Languageen
FieldComputer Science
TopicEducational Systems and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsVisionSpace (punctuation)Core (optical fiber)Library scienceSociologyComputer sciencePolitical scienceTelecommunications

Abstract

fetched live from OpenAlex

최근 대학도서관을 둘러 싼 변화의 속도는 전통적 대학도서관의 역할과 함께 가상공간에서의 통합서비스를 지원하는 하이브리드 도서관 환경을 요구하고 있다. 이러한 변화에 따라 대학도서관은 핵심 가치를 중심으로 그 사명과 비전을 검토 할 시점에 직면해 있다. 본 연구는 미국, 영국, 캐나다, 호주 등 대학도서관의 사명과 비전 선언서의 내용을 핵심 키워드를 중심으로 분석하였다. 이를 통하여 선진 대학도서관의 사명과 비전 선언서에서 제시하고 있는 핵심 가치가 무엇인가를 도출하고자 하였다. 아울러 선진 대학도서관들이 추구하고 있는 핵심 개념을 장서 및 정보자원, 서비스, 장소, 사람, 환경 및 기반시설, 커뮤니티, 모체 대학과의 관계 등으로 대별하고, 각 영역별로 그 핵심 가치를 제시하였다. 궁극적으로 본 연구는 아직 사명과 비전 선언서 수립이 일반화되지 않은 우리나라 대학도서관에 사명과 비전 수립을 위한 방향 제시와 기초 자료를 제공하고자 하였다. Recently, the speed of changes surrounding university libraries requires a hybrid library environment that supports integrated services, combining a virtual space with physical space of traditional libraries. Through changes, university libraries have faced the need to review their missions and visions, laying stress on the core values of university libraries. This study analyzed the central keywords in the contents of mission and vision statements of university libraries in America, England, Canada, and Australia. The research was acquired from mission and vision statements containing core values of developed university libraries. Core values are present in each area such as correction and information resources, services, place, people, environment and infrastructure, community, and parent-university relations. Ultimately, this study tried to provide the directions and the basic materials for the establishment of missions and visions for university libraries in Korea.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.022
Open science0.0010.000
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 head0.030
GPT teacher head0.275
Teacher spread0.245 · 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 designTheoretical or conceptual
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

Citations0
Published2005
Admission routes1
Has abstractyes

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