Limited consensus around ARM information protection practices
Bibliographic record
Abstract
Archives and Records Management (ARM) literature surrounding Information Protection (IP) has been developed in relative isolation from the IP field. As a result, it has been unclear until now whether and to what extent ARM literature and practice is consistent with or divergent from IP literature and practice. This paper compares IP and ARM information protection through the lens of a Standard of Practice (SoP). An existing enterprise IP SoP was adapted to ARM through literature analysis and produced a draft ARM SoP. The draft ARM SoP was applied in a rote fashion to a small sample of government-operated archives to identify likely areas of consensus and lack of consensus surrounding the various elements of the SoP. This resulted in some areas of strong consensus and other areas of strong divergence. A horizontal element was also used to identify whether and to what extent learning and thinking about the issues caused changes in evaluation. Increased consensus was found after a delay between initial exposure to the SoP and subsequent review of SoP elements. While this is a small sample study, it points toward both the need and the value of larger and more comprehensive studies in order to afford a clear consensus around reasonable and prudent practices for ARM IP and the value of additional awareness, training, and education in IP issues within the ARM community.
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 | 0.237 | 0.343 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".