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A Comparative Study on the Codes of Ethics by Professional Associations of Libraries, Archives and Museums in Foreign Countries

2014· article· en· W1973370639 on OpenAlexaboutno aff
Ji-Hyun Kim

Bibliographic record

VenueJournal of the Korean BIBLIA Society for library and Information Science · 2014
Typearticle
Languageen
FieldMedicine
TopicPatient Dignity and Privacy
Canadian institutionsnot available
Fundersnot available
KeywordsStewardship (theology)Professional ethicsEthical codeProfessional associationInterpretation (philosophy)Intellectual freedomProfessional studiesProfessional conductLibrary sciencePublic relationsSociologyPolitical scienceProfessional developmentLawPedagogyComputer science

Abstract

fetched live from OpenAlex

본 연구는 도서관, 기록관, 박물관의 협력에 있어 실무자들의 업무와 인식의 근간이 되는 전문직 가치를 논의하고 윤리강령 분석을 통해 세 분야에서 공통적으로 제시되는 전문직 가치와 각 분야에서 차별적으로 제시되는 전문직 가치를 조사하는 것을 목적으로 하였다. 이를 위해 미국, 캐나다, 영국, 호주의 도서관, 기록관, 박물관 협회 윤리강령을 조사대상으로 선정하였다. 문헌연구를 바탕으로 전문직 가치에 대한 이론적 연구를 조사하여 공통된 가치를 추출하였으며 이를 근거로 분석을 실시하면서 최종적으로 13개의 공통 요소를 포함하는 분석 기준을 제시하였다. 분석 결과 공통된 전문직 가치로는 접근, 개인정보 보호, 소장물 관리, 전문직으로서의 임무, 사회적 책임인 것으로 나타났다. 도서관 분야에서는 지적자유, 기록관 분야에서는 증거로서의 기록, 박물관 분야에서는 연구 해석 기능이 각 분야에서 고유하면서도 강조되는 윤리적 측면인 것으로 나타났다. This study investigated professional values on which the practices and the perceptions of professions were based, then identified common professional values and different ones. The codes of ethics by professional organizations of libraries, archives, or museums in the U.S., Canada, the U.K. and Australia were selected for the analysis. Predicated on the literature review, common professional values were derived from the existing studies. While conducting the analysis, 13 values were identified and finalized. As a result, it was found that common professional values included access, privacy, stewardship, professional conduct and professional responsibilities to the society. Intellectual freedom in the library area, records as an evidence in the archive area, and research and interpretation in the museum area are those emphasized and unique in each area.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.005
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 head0.074
GPT teacher head0.331
Teacher spread0.257 · 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.

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
Published2014
Admission routes1
Has abstractyes

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