Using information visualization and visual analytics to achieve a more sustainable future for archives: A survey and critical analysis of some developments
Bibliographic record
Abstract
This paper provides a survey of some developments in the application of information visualization and visual analytics within the field of archives. The paper begins by discussing the origins and development of information visualization and visual analytics, followed by an explanation of their differences. It then moves on to a critical analysis of the literature on the application of these approaches within the field of archives, arguing that more attention should be paid to applying these technologies to unprocessed archival material than to the output of archival analysis. The paper also contends that greater emphasis is needed in the research on analyzing the cognitive tasks of archivists and of different types of users to create ‘snug’ interfaces as opposed to ones that are just ‘generous’. The paper further calls for more formal evaluation of the efficacy of different tools in relation to claims made about them. Finally, the paper calls for greater critical reflection in the literature on the ways i...
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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 | 0.025 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".