Bibliographic record
Abstract
Purpose Digital records appraisal and aspects of archival values in theory, regularization and practice are explored. This paper aims to reflect upon the appraisal process, responsibility and norms for value creation in a digitalized environment. The research question was how do appraisal theory, normative rules and appraisal practice meet the aims of values in digital archives? Design/methodology/approach The study triangulated appraisal theory, normative values and participants’ views about archival values in appraisal practice in a Swedish setting. Content analysis were used to explore normative documents and interviews. Appraisal theories of the Swedish Nils Nilsson and the Canadian Terry Cook were interpreted. The result was related to theories on public values, the nature of responsibility and relations between the state and citizens. Findings The results show influences between theory, norms and practice. Changes in norms and practice do not follow the development of digitalization. Responsibility is focused on tasks, which exposes risks of accountability control and knowledge of appraisal grounds. The paper concludes that access requirements and user needs may prompt change in appraisal processes. In the light of digitalization, “primary and secondary value” are merely a matter of use and usability in a time and space (continuum) perspective. Research limitations/implications This study is based in Sweden where extensive right of access to public records and default preservation are norm. Originality/value The result shows how allocated responsibilities impinge on a re-active digital appraisal process.
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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 | 0.090 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.009 | 0.071 |
| Scholarly communication | 0.026 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".