Engaging with justice in integrated landscape approaches
Bibliographic record
Abstract
Climate and biodiversity crises, conflicts over access to land, water, or food, multiple and overlapping types of land management and livelihoods are some of the players that describe current landscape challenges worldwide. It has been broadly acknowledged that addressing interconnected social and ecological challenges needs integrated solutions at landscape scale. Integrated landscape approaches (ILAs) are governance strategies that deal with these complex social and ecological challenges. Yet, many of these governance strategies lack a nuanced attention to the injustices that manifest themselves in landscape governance, use, and management. These injustices influence the strategies chosen and how they can be reached. In this synthesis, we first identify the injustices that can appear in, and shape a given landscape, empirically illustrating how ILAs can relate to multiple dimensions of justice. We highlight methods suitable for studying injustices in landscapes from an academic perspective. Later, we share and reflect about our positionality, and our experiences of struggling, in harnessing a more transgressive science that engages with landscape justice. We argue that identifying, understanding, and reflecting on how to address injustice in landscape research should become a crucial step in implementing ILAs.
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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 | 0.033 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.073 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".