“I’ll Be Home for Christmas”: The Role of International Maasai Migrants in Rural Sustainable Community Development
Bibliographic record
Abstract
While the Maasai have to be among sub-Saharan Africa’s most mobile population due to their traditional transhumant pastoral livelihood strategy, compared with other neighboring ethnic groups they have been relatively late to migrate in substantial numbers for wage labour opportunities. In the community of Elangata Wuas in Southern Kenya, international migration for employment abroad has been very rare but promises to increase in significant numbers with the dramatic rise in education participation and diversification of livelihoods. Drawing on long-term ethnographic research and the specific experiences of the few international migrant pioneers in Elangata Wuas, this paper explores how community members assess the impacts of international migration on community sustainable development. It appears that international migration facilitates, and even exacerbates, inequality, which is locally celebrated, under an ethic of inter-dependence, as sustainable development. Particular attention is paid to the mechanisms of social control employed by community members to socially maintain their migrants as part of the community so that these migrants feel continued pressure and commitment to invest and develop their communities. Such mechanisms are importantly derived from the adaptability and accommodation of culture and the re-invention of tradition.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".