A Study on Appraisal and Selection Indicators Based on Content Analysis of International Archival Appraisal Policies
Bibliographic record
Abstract
The goal of archival appraisal is to assist archives in selecting and preserving information of permanent value in accordance with established principles, to efficiently enhance archival management and maximize the value of archives. Archival appraisal is an important procedure for the construction of archives collections. This study utilizes content analysis. First, it constructs an indicator classification framework based on research papers related to archival value issues. Subsequently, it examines the archival appraisal policies of six countries, namely the United States, Canada, the United Kingdom, the Netherlands, Australia, and New Zealand. It analyzes the emphasis on archival value and policy characteristics in these six countries and summarizes core indicators used in archival appraisal. These findings serve as a reference for the archival practice field to improve archival appraisal operations and archival selection competencies for administrative agencies.
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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 | 0.101 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".