Visual analysis of information world maps: An exploration of four methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.051 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it