Visualizing information in the records and archives management (RAM) disciplines
Bibliographic record
Abstract
Purpose This paper aims to explore the concept of information in records and archives management (RAM) from a fresh, visual perspective by using arts-informed methodology and the draw-and-write technique. Design/methodology/approach Students and practitioners of RAM in Australia were asked to answer the question, “what is information?” in a drawing and then to describe the drawing in words. This produced a data set of 255 drawings of information or “iSquares”, for short. Compositional interpretation and a framework of graphic representations by Engelhardt were applied to determine how participants envision information and what the renderings imply for RAM. Findings The images reveal an overwhelming recognition in RAM of the diversity of media formats of information and the hyperconnectivity of information in networked information systems; and illustrate the central place of human beings within these systems. These findings offer striking, accessible illustrations of major concepts in RAM and enable new understandings through the construction of stories. Practical implications There are both pedagogical applications and practical implications of this work for students, practitioners and knowledge workers. The graphical representations of information in this research deepen the understanding of textual definitions of information. The data set of iSquares provides opportunities to create new storyboards to explain information definitions, practices and phenomena in RAM disciplines, and, to explain related concepts such as data, information, knowledge and wisdom hierarchy. Originality/value This is the first study in RAM disciplines to provide visual illustrations of information using graphical image representations.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".