Embracing uncertainty: rethinking migration policy through pastoralists’ experiences
Bibliographic record
Abstract
Today there is a disjuncture between migration flows that are complex, mixed and constantly evolving and the emerging global migration governance paradigm that seeks to impose clarity, certainty, regularity and order. Addressing the gap between policies and realities, this article explores lessons for migration policy and governance from mobile pastoralists' experience. Using examples from human migration flows within and between Europe and Africa and insights from pastoral systems from India, Italy and Kenya, the article identifies important similarities between international migration and pastoral mobility. We focus on four interconnections: both international migration and pastoral mobility show multi-directional and fragmented patterns; both involve multiple, intersecting socio-economic, political, cultural and environmental drivers; both must respond to non-linear systems, where critical junctures and tipping points undermine clear prediction and forecasts, making social navigation and reliability management more useful concepts than risk-based prediction and control and finally for both uncertainty is not conceived of as a state of crisis but an inherent feature, pregnant with possibility and hope. Building on these four points, and drawing from pastoralists' experiences, we propose some methodological, practical and policy reflections for bridging the disjuncture between migration realities on the ground and global migration governance policies and discourses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.066 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.013 |
| 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".