Making Landscapes Negotiable: Q-methodology as a Boundary-Spanning and Empowering Diagnostic
Bibliographic record
Abstract
Landscapes are conceptually fuzzy and rich, and subject to plural framings. They are places of inquiry and intervention for scientists and practitioners, but also concepts bound to peoples' dynamic identities, knowledge systems, inspiration, and well-being. These varying interpretations change the way landscapes function and evolve. Developed in the 1930s, Q-methodology is increasingly recognized for being useful in documenting and interrogating environmental discourses. Yet its application in the context of how integrated landscape approaches better navigate land-use dilemmas is still in its infancy. Based on our experience and emerging literature, such as the papers in this special collection, this article discusses the value of Q-methodology in addressing landscape sustainability issues. Q-methodology helps unravel and communicate common and contradicting landscape imaginaries and narratives in translational and boundary-spanning ways, thus bridging actors' different understandings of problems and solutions and revealing common or differentiated entry points for negotiating trade-offs between competing land uses. The methodology can be empowering for marginalized people by uncovering their views and aspirational values to decision-makers and policymakers. We argue that this potential can be further strengthened by using Q to identify counter-hegemonic discourses and alliances that combat injustices regarding whose knowledge and visions count. In this way, applying Q-methodology in integrated landscape approaches can become a key tool for transitioning toward just, inclusive, and sustainable landscapes.
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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 | 0.128 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.009 | 0.080 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".