Communication for Conflict Resolution: the Pashtun Tribal Rhetoric for Peace Building in Afghanistan
Bibliographic record
Abstract
Focusing on communication as an important means besides other efforts for conflict resolution in an asymmetric armed conflict in Afghanistan, this study looked for a rhetorical communication approach appropriate to Pashtun tribal setting in South-eastern (Loya Paktya region) Afghanistan. The study explored and found some perceived essentials of such persuasive communication by conducting face-to-face semi-structured in depth interviews with 17 participants. Thematic analysis was used to code and categorize data. Aristotle’s rhetorical theory provided a framework for this qualitative study by narrowing down the focus to exploring credibility of the communicator (ethos), the rationality of the message (logos), and the emotional appeals (pathos), particular for the south-eastern Pashtun tribal setting, during communication. In addition, considering the relation between rhetorical and soft power theories in influencing the choice of an audience, this project also asked participants if and how communication in their tribal setting could be framed as an influencing power by attraction rather than by coercion. Therefore, soft power of which persuasive communication is a crucial part was also used as a theoretical framework for this study. The findings show the significance of persuasive communication in future conflict resolution efforts in Afghanistan.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".