From liberal peace to Ubuntu: The turn needed for effective conflict transformation in Mozambique
Bibliographic record
Abstract
This thesis examines Mozambique’s violent political conflict by exploring why it remains locked in a protracted conflict despite the 1992 and 2019 peace agreements. These peace agreements were developed to address the protracted violent political conflicts that have enveloped the country since its independence from colonial Portugal in 1975, conflicts that trace their origins to colonization. The 1992 and 2019 peace agreements were unsuccessful in bringing sustainable peace to Mozambique because they were exclusively founded on liberal peace principles. In addition, surveying both agreements this study notes that the negotiation of both agreements prioritized statebuilding and political peace, while glossing over the contextual root causes of the conflict, marginalizing the local actors and peacebuilding dynamics that ensure social peace. Therefore, this thesis argues that Ubuntu, which is based on people’s local knowledge and embedded in the culture of Indigenous people, can contribute to building sustainable peace in Mozambique as part of a multi-track peacebuilding process. The marginalized Ubuntu indigenous peacebuilding philosophy and practice embodies qualities such as humanness and empathy and is founded on the values of unity, justice, ethics, and education and is critical to guide the peace efforts in Mozambique. I arrived at these arguments by analyzing both peace agreements guided by frameworks such as social cubism, Critical and Emancipatory Peacebuilding (CEP), and Critical Qualitative Methodology (CQM) that embodies autoethnography. Autoethnography as a research method, assists in systematically exploring my stories, life, and work experiences as an academic growing up in a war zone to understand my cultural and social experiences of war and peace.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".