Practices in Progress: The State of Reappraisal and Deaccessioning in Archives
Bibliographic record
Abstract
In the spring of 2017, the article authors conducted a survey of archival institutions in the United States and Canada regarding current reappraisal and deaccessioning practices. The first of its kind in the United States, the survey gathered quantitative data regarding how, why, and which archival repositories reappraise and deaccession. This article describes the survey method, questions asked, and data collected, and provides an analysis of the results. The authors sought to learn if resources influence these practices; what, if any, policies and guidelines exist locally; how the processes are carried out; how archivists perceive ethical concerns commonly associated with these practices; and what benefits and consequences result from reappraising and deaccessioning. They found that reappraising and deaccessioning are common practices throughout a variety of institutions and result in positive outcomes. However, misunderstanding remains about these practices, and institutions may not always be conducting these practices in an ethical and responsible manner.
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 | 0.053 | 0.150 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.013 | 0.041 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| 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".