Peace, Culture and Communication: “Languaging” Post-conflict Disputes
Bibliographic record
Abstract
Language, which is rarely neutral, shapes perception and behavior. Consequently, it plays an important role in relation to conflict and peace. The language of conflict usually functions on the basis of using differences to promote violence. Interviews conducted on land disputes in the post-conflict context of Northern Uganda, showed that language can be used to reduce these differences and affirm dignity thus diffusing tensions. Our preceding studies of conflict discourse within returnee communities have endeavored to show how language use, by imposing certain misrepresentations as legitimate, undermines efforts of social reintegration, perpetuates conditions of negative peace and can pose a threat of returning to conflict.In this study of Gulu elders dealing with post-conflict disputes, language is perceived as a tool of positive peace. Borrowing from the sociocultural theory of mind and its application to concepts of language, the paper shows how language can foster open and inclusive communication and support the pursuit of peaceful cohabitation within returnee communities. It goes on to demonstrate how language, within the cultural institutions of returnee communities, constitutes power that can be used in “languaging” conflict resolution. According to the study, language has embedded within it actual relations of power, so much so that those who control it exercise an enormous influence on how the communities perceive conflict and peace-building and what behaviors they accept in relation to resolving post-conflict disputes.Consequently, the quick revitalization of traditional arrangements of dispute settlement has been possible in the area of Gulu because language is a strong social institution which has enhanced the efforts of peace maintenance in the Acholi post conflict context. Languaging or talking through disputes as an alternative discourse to conflict should be embraced as a strategy of empowering the voiceless. It is an effective and sustainable cost effective strategy for dealing with cyclic disputes especially when applied as complementary to other dispute settlement approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.186 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".