Memories of Mogadishu: Reconstructing post-conflict societies through memory and storytelling
Bibliographic record
Abstract
For many members of the Somali diaspora, the fear of fading memories places a sense of urgency on them to keep these stories of their homeland alive. The great African novelist Ben Okri once said, “to poison a nation, poison its stories”. Stories have the ability to harm or heal societies. Oftentimes, it is simply exclusion from the main narrative that can greatly harm or marginalize a group of people. This paper examines the use of memory in the reconstruction of a once cosmopolitan city by the Somali diaspora around the world through the Memories of Mogadishu initiative. The film by the same title is a short documentary made by the author, in which she interviews nine members of the Somali diaspora currently residing in Canada. Ultimately, this project and this paper reveal the realities of how post-conflict societies, and individuals within them, reconstruct and reconcile their memories, in this case of their former home of Mogadishu, Somalia. This paper analyses the nine interviews and is divided into the following four sections: “Memories of Mogadishu before the Civil War”, “Civil War and Leaving Mogadishu”, “Identity Revision, Memory, and Routinization”, and “Losing and Rebuilding Memories of Mogadishu (and Themselves)”.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".