Framing Experience: Concept Maps, Mind Maps, and Data Collection in Qualitative Research
Bibliographic record
Abstract
Traditionally, qualitative data collection has focused on observation, interviews, and document or artifact review. Building on earlier work on concept mapping in the social sciences, the authors describe its use in an exploratory pilot study on the perceptions of four Canadians who worked abroad on a criminal justice reform project. Drawing on this study, the authors argue that traditional definitions of concept mapping should be expanded to include more flexible approaches to the collection of graphic representations of experience. In this way, user-generated maps can assist participants to better frame their experience and can help qualitative researchers in the design and development of additional data collection strategies. Whether one calls these data collection tools concept maps or mind maps, for a generation of visually oriented social science researchers they offer a graphic and participant-centric means to ground data within theory.
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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 | 0.168 | 0.270 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.011 | 0.062 |
| Scholarly communication | 0.022 | 0.036 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".