A Study on Analyzing Demands for Professional Librarians in Domestic and Foreign Countries
Bibliographic record
Abstract
본 연구에서는 국내외 전문사서에 대한 수요조사 분석을 수행함으로써 전문사서의 유형을 개발하고자 하며, 이러한 전문사서들이 갖추어야 할 최소한의 자격요건을 제시하고자 한다. 이를 위해 미국, 영국, 캐나다의 사서 구인사이트 조사 분석을 통한 수요조사와, 국내 현장사서들을 대상으로 전문사서에 대한 수요조사를 실시하여, 전문사서 유형을 제안하고자 한다. IFLA 및 ALA에서 정의하는 전문사서의 자격요건을 조사하고 실제 구인기관에서 전문사서에 대하여 요구하는 자격요건을 조사하였다. 연구결과, 국내외 수요분석을 통하여 전문사서 유형을 주제별(7종), 기능별(12종), 대상별(2종)로 추출하였으며, 전문사서의 자격요건을 다음과 같이 제안하였다: (1) "학부 혹은 석사과정에서 문헌정보학을 전공한 석사학위 소지자로서 도서관 관련 6년 이상의 경력 중 2년 이상의 해당분야 경력자," (2) "2급 정사서로서 총 9년 이상의 도서관업무 담당 경력 중 2년 이상의 해당분야 경력자로서 해당 교육과정 이수자." This study aims to deane prototypes of professional librarians as well as their job requirements by analyzing international demands for librarianship. Demands analysis starts with examining librarian job posting websites in the US, UK and Canada, followed by inquiring incumbent librarians in Korea. As for the job requirements. the study parallels definitions by IFLA and ALA with job announcements by hiring institutions. As a result, we identified 21 professional librarian prototypes by subject(7), task(12) and object(2), and defined the job requirements as 1) MLS or equivalent school knowledge with undergraduate degree of library science, and/or at least six years of relevant business experience, or 2) at least nine-rear experience as a second-level full-time librarian with two-rear experience and/or professional training in the job relevant field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".