Visual analysis of information world maps: An exploration of four methods
Bibliographic record
Abstract
Information researchers increasingly use participatory, arts-based methods to better understand the social contexts of individuals and populations. However, it remains rare to engage in qualitative analysis of the resulting visual artefacts. This article explores approaches to analysing visual media generated through a specific arts-based method, information world mapping (IWM), an interdisciplinary draw-and-talk technique that elicits data about individuals’ social information worlds. Here, we test four approaches to analysing visual media generated through IWM: directed qualitative content analysis (QCA), compositional interpretation, conceptual analysis and visual discourse analysis using situational analysis (SA). QCA was effective in creating an overview of participants’ information practices, yet raised concern regarding interpretive bias. Using an inductive taxonomy for compositional interpretation, we identified genre conventions for IWMs. Conceptual analysis resulted primarily in a reflection of the research procedures and epistemology. SA, while time-consuming, generated a large amount of rich data, including discourses and power relations that were not identified in previous analysis of textual data. In a reversal of our previous stance that cautioned against IWM analysis, we encourage other researchers to consider integrated or secondary visual analysis of IWMs.
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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 | 0.046 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".