Where is civil society? Where are the women? Barriers to inclusion in Cameroon’s national dialogue
Bibliographic record
Abstract
This paper is grounded in the prevalent underrepresentation of civil society, women and other politically marginalised local actors in peacebuilding. While national dialogues are locally driven peacebuilding projects that can bring together a diverse range of local actors, existing power dynamics and structural inequalities have hindered their effectiveness and inclusivity in Cameroon. From the lenses of the local turn in peacebuilding, this study explores and addresses power imbalances among key actors, particularly between more powerful and less powerful groups. Drawing on 78 interviews, secondary data, and stakeholder analysis, the study explored barriers to inclusivity in Cameroon’s national dialogue aimed at resolving the Anglophone Crisis and evaluated its outcomes. The paper identified entrenched power imbalances that emerged because of party politics, exclusionary practices, patriarchy, inequalities, and marginalisation of alternative voices at the local and national level in Cameroon. It argues that these cultural and structural barriers continue to impede meaningful inclusion and participation in local peacebuilding. The paper suggests that for locally driven peace processes such as national dialogue to be inclusive, national actors and development stakeholders must work to dismantle these barriers.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".