The dynamics and politics of integrating local knowledge systems in multistakeholder platforms
Bibliographic record
Abstract
Multistakeholder platforms (MSPs) enhance collaborative decision making in environmental and landscape governance. However, the dynamics of integrating local knowledge systems and empowering local knowledge holders remain under-researched. Using data from semi-structured interviews with participants and non-participants in two MSPs in Zambia, this study examines how various forms of power impact what kind of knowledge is exchanged, who engages in knowledge exchange, and whether that leads to context- and culture-specific, sustained, committed, and empowering knowledge co-production. Findings reveal that knowledge exchange and co-production are poorly developed or absent and subject to various power dynamics. The private sector often disengages from MSP processes and uses hidden power to prioritize its agenda. Government actors exercise visible power based on their rule-making and operational power. Local knowledge holders are the most marginalized and underrepresented actors, constrained by invisible power stemming from a complex interplay of structural, discursive, and framing power. This marginalization leads to miscommunication, misrepresentation, and limited influence on decision making, along with unclarified rights. NGOs partly counteract these imbalances by leveraging countervailing power to challenge internalized invisible power that hinders marginalized groups from expressing their views. Despite narratives advocating for local knowledge inclusion and more equitable collaborative processes, both MSPs show limited progress in fostering meaningful knowledge interaction and influence for local knowledge holders. Addressing these issues requires fundamental changes in knowledge governance, including fostering the inclusion of marginalized knowledge holders, adopting pluralistic approaches, committing to knowledge co-production, and tackling power imbalances. This implies a critical role for civil society organizations in amplifying the voices of marginalized groups and advocating for the inclusion of local knowledge into MSPs and decision making. Further research needs to explore the politics of knowledge governance with particular attention to how discursive and framing power influences the empowerment or suppression of marginalized knowledge systems and their holders.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".