A Comparative Study on Appraisal Policies of National Archives of UK, USA, Canada, and Australia
Bibliographic record
Abstract
평가작업은 기록관리 업무에서 가장 중요한 기능 중에 하나이다. 즉, 기록관리에 있어서 평가란 기록물의 가치를 평가하는 것으로, 그 결과 지속적으로 유지 보존되어야 하는 업무적 가치를 지니거나 지속적 활용성을 지닌 기록을 체계적으로 선정하고, 그 보유기간을 결정할 수 있다. 현재 엄청난 양의 기록물이 생산되는 환경에서 선별의 범위와 방식을 규정하여 효용의 극대화를 추구하게 하는 기록물평가의 중요성은 더욱 부각된다고 볼 수 있다. 본 연구는 각국(영국, 미국, 캐나다, 호주)의 국립기록원이 제정한 영구적 기록물선정과 폐기를 위하여 수립된 평가정책을 1) 평가정책의 목표, 2) 평가목적, 3) 평가기준, 4) 평가절차, 5) 특별 고려사항 등 다섯 가지 항목에 대해서 분석하여 비교하였다. 또한, 본 연구는 법령으로 국가기록물을 체계적으로 선별 수집하기에는 역부족이라 생각하여 국가기록원이 국가기록물을 평가ㆍ선별하는 데 도움을 줄 수 있는 평가정책을 개발하는 것이 바람직하다고 보고, 평가정책 수립 시 고려사항을 제시하였다. Appraisal is one of the most important record management tasks. Appraisal is the process of evaluating business activities to determine which records need to be captured and how long the records need to be kept to meet business, the requirements of organizational accountability and community expectations. Appraisal, therefore, is concerned with deciding both what records should be created to document a business activity and how long those records should be retained. This paper attempts to review the appraisal policies of four national archives in UK, USA, Canada, Australia, which have managed public records systematically and to compare appraisal policies in the following five parts such as 1) purpose, 2) appraisal objectives, 3) appraisal values, 4) procedures for appraisal, and 5) special considerations. Finally, the paper suggests the important items required when the Korean National Archives and Records Services develops their own appraisal policies.
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 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 | 0.034 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".