‘Bonkers but good!’ – Using illustration-based interview methods to understand land management and conservation visions
Bibliographic record
Abstract
Visions help to understand common ground and tensions among citizens and stakeholders, supporting inclusive land management and conservation solutions to the climate emergency and biodiversity crisis. With careful design and sufficient resource, it is possible to bring together communities and other stakeholders to share perspectives and deliberate desired futures, identifying more acceptable alternatives and avoiding costly delays. We evaluated researcher and participant experiences of illustration-based interviews to understand land management visions using four studies in Scotland, The Netherlands and Spain. These studies used STREAMLINE, a visual mixed-method interview format using thematic illustrated canvases designed to provide an inclusive and creative framing for participants to contemplate their desired future. Participants enjoyed the informal visual format, which reduced pressure, increased comfort through the research process, and helped their thinking and reflection about complex topics. They also valued being listened to and having the opportunity to share their views. Researchers appreciated the ability to triangulate rich qualitative data with a variety of quantitative measure through the mixed-method format and the flexibility to adapt the canvases to suit their research aims. Positive participant experience made facilitation easier and was stimulating for the researchers. The credibility and legitimacy of illustration-based interviews will ultimately depend on specific research design-decisions and testing, which can make the approach more resource intensive than conventional interviews. While organisational barriers should be considered realistically, illustration-based interviews can have high saliency by providing useful and usable insights that strengthen land management policy and planning.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".