Everyday mobility and changing livelihood trajectories: implications for vulnerability and adaptation in dryland regions
Bibliographic record
Abstract
Dryland regions are highly dynamic environments in which multiple pressures intersect, threatening livelihood security. Mobility is an integral feature in these environments and represents a key risk management strategy for people to respond to frequent livelihood shocks and stresses. Global environmental change scholarship has tended to articulate spatial and temporal change inadequately, portraying populations in a way that belies their socially differentiated and inherently mobile livelihoods. We explored the role of mobility as an ongoing, “everyday” adaptive response to changing environmental, economic, and social conditions. We draw on 21 Life History (LH) interviews to explore the drivers and outcomes of people’s mobility behavior in drylands of Ghana, Kenya, Namibia, and India. We present the adaptation option space (AOS) as a novel theoretical development to explore livelihood trajectories. Within our cases, we found that mobility was ubiquitous and facilitated changes to and exchanges within people’s risk profiles in three main ways: novelty (risks gained or lost), modification (risks attenuated or accentuated), and no change. Temporal analysis showed three broad trajectories in people’s lives set within broader structural constraints: upward, downward, and stable, depending on people’s abilities to manage their AOS. The analysis confirmed that the AOS was a useful heuristic to understand how people exert agency to respond to an array of converging risks while negotiating broader drivers of change. Moreover, the data demonstrated how compounding shocks had negative impacts on people, highlighting the value of temporally-sensitive approaches.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".