(How) should information world maps be visually analyzed?
Bibliographic record
Abstract
ABSTRACT Information world mapping (IWM) is a participatory, arts‐based elicitation method for use in interviews about information behaviours or practices. The participant‐drawn maps that result from this technique are typically used as ancillary data sources to aid in analysis of interview transcripts; however it may be possible, and even useful, to analyze these maps as data independent of the interviews, using methods suited to image analysis. This brief paper and accompanying visual presenta‐ tion describe results of analyses of information world maps using four different and contrasting methods with accompanying examples, and issue recommendations regarding the use of qualitative content analysis, compositional analysis, conceptual analysis, and visual discourse analysis, for analysis of information world maps. These insights may be generalized to other objects (e.g., photos) created through arts‐based elicitation interviewing.
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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 | 0.021 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".