Invisible or Networked? Exploring Dynamics of Social Capital and Networking Among Urban Refugees in Dar es Salaam, Tanzania
Bibliographic record
Abstract
Policy in refugee hosting states plays a significant role in how urban refugees plot their exile strategies.However, there is often a divide between de jure interpretation of refugee policy as written and the de facto manner in which it is experienced.As such, there is significant variation in how refugees manage their social networks in exile.This thesis analyzes the social capital and livelihood procurement strategies of urban refugees in Dar es Salaam, Tanzania.It will illustrate how capital and pre-exile experiences create variation in social and livelihood strategy in exile, as well as demonstrate how political factors are limited in accounting for strategy differences.Refugees demonstrate agency by navigating the structural landscape of Tanzania.While refugees are regarded as a uniform group, variation in how networks are formed, maintained and employed exists between sub-groups, conditioned by relative accumulation of capital.The unequal possession of resources and capital creates differences in access to space, creating a distinct geography of refugee space.Given the multitude of refugee groups that inhabit the city, Dar es Salaam provides a rich setting for exploring the drivers of these social nuances and exploring how refugee spaces are conditioned.
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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 | 0.001 | 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 | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".