Information world mapping: A participatory, visual, elicitation activity for information practice interviews
Bibliographic record
Abstract
Abstract In an increasingly visually‐oriented world, researchers within and beyond LIS can benefit from exploring, developing and applying creative methods for data collection and research participant engagement. Participatory, arts‐involved methods can complement more traditional elicitation techniques, generating rich data that allows researchers to explore participant experiences with socially‐ and culturally‐constructed information practices. This poster presents a novel drawing‐based elicitation technique, Information World Mapping (IWM), which was developed to augment traditional qualitative interview methods. IWM combines elements of three established arts‐involved methods: information horizons (Sonnenwald, Wildemuth, & Harmon, ), relational mapping (Radford & Neke, ), and Photovoice (Wang & Burris, ). Aiming to enable creative communication about the information world of the research participant as it relates to a social process of interest (e.g., making a health decision or completing a work‐related task), IWM guides participants in generating drawings or maps of their personal information worlds, including key interpersonal and institutional relationships. These drawings are then used to facilitate elicitation of participants' own stories about, and interpretations of, their information practices. A case example of IWM in practice is provided, based on a study of teenage parents' health‐related information practices.
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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 | 0.049 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".