Missed or misused opportunity? Social cohesion, national dialogue and peacebuilding in Cameroon
Bibliographic record
Abstract
This paper uses the multidimensional aspects of social cohesion which comprises different elements of trust, equality, inclusion, and participation, to show how political actors can impede efforts to foster social cohesion in conflict affected societies. It identifies key opportunities that were either missed or misused by elites during the national dialogue process intended to resolve Cameroon's Anglophone crisis. Historically, the relationship between elites and the Anglophone population in Cameroon has been characterised by fragile social cohesion due to marginalisation experienced in Anglophone communities. The actions and power-preserving efforts of elites have exacerbated inequalities, political exclusion and mistrust, thereby affecting social cohesion. Based on field observation, semi-structured interviews and secondary data, this paper identifies and analyses five scenarios where Anglophone elites could have acted differently to enhance social cohesion, but failed to do so. Rather, their actions towards the population further weakened social cohesion and facilitated the instrumentalisation of the 2019 national dialogue in Cameroon. Considering the unique characteristics of this case, future dialogue processes, especially those addressing marginalisation concerns, should prioritize strengthening social cohesion and refrain from overlooking its relevance by merely treating it as a desired outcome of a dialogue process.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".