Navigating power imbalances in landscape governance: a network and influence analysis in southern Zambia
Bibliographic record
Abstract
Abstract Actors engaging in integrated landscape approaches to reconciling conservation and development represent multiple sectors and scales and actors with different powers, resource access, and influence on decision-making. Despite growing acknowledgement, limited evidence exists on the implications of power relations for landscape governance. Therefore, this paper asks why and how different forms of power unfold and affect the functioning of multi-stakeholder platforms in southern Zambia. Social network analysis and a power influence assessment reveal that all actors exercise some form of visible, hidden, or invisible power in different social spaces to influence decision-making or negotiate a new social order. The intersection of customary and state governance reveals that power imbalances are the product of actors’ social belongingness, situatedness, and settlement histories. We conclude that integrated landscape approaches are potentially suited to balance power by triggering new dynamic social spaces for different power holders to engage in landscape decision-making. However, a power analysis before implementing a landscape approach helps better recognise power differentials and create a basis for marginalised actors to participate in decision-making equally. The paper bears relevance beyond the case, as the methods used to unravel power dynamics in contested landscapes are applicable across the tropics where mixed statutory and customary governance arrangements prevail.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".