Social, cultural and political responses to Somaliland’s <i>tahriib</i> movement
Bibliographic record
Abstract
Irregular migration to Europe has surged over the past decade, including tahriib (irregular migration) from Somaliland, an internationally unrecognized nation-state in East Africa. As a unrecognized state, Somaliland endeavours to forge its national identity by drawing upon collective memories of historical violence and has gained prominence in the region through its successful forms of communal and indigenous forms of state-building, security and peace against a backdrop of the continued violence in southern Somalia. Consequently, Somalilanders that participate in tahriib are often viewed as unpatriotic for leaving their homeland and risking their lives to venture to foreign lands, exacerbating Somaliland’s precarious statehood. The article argues that the rise of particular language and terminology in Somaliland to disparagingly depict the tahriib movement challenges notions of national solidarity between migrants and their home country. Understanding Somaliland within this context highlights the particularities in which the Somaliland tahriib movement operates in and challenges notions of solidarity between tahriib participants and their country of origin. Primarily based on fieldwork from 2022 to 2023, including interviews with tahriib participants, their family members, community leaders and civil servants, this article presents an analysis of the political, social, and cultural responses towards tahriib participants.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".