Multi-directional migration, land ownership and livelihood strategies in the Peruvian Andes: conceptualising urban-rural return flows during the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has affected human mobility dynamics worldwide. In Peru, thousands of people left the cities to return to their rural homelands, which resembled characteristics of a historical reversal of internal rural-urban migration flows. This study challenges and contextualises such a conceptualisation as a reversal through an ex-post analysis of pandemic urban-rural returns between 2020 and 2022 in the Peruvian Andes, drawing on theoretical approaches from migration studies, rural livelihoods and peasant studies. The qualitative case study with ethnographic fieldwork in the department of Cusco offers two contributions: First, it empirically illustrates rural individual agency behind the massive pandemic population flows in the Andes for new insights on internal mobility, livelihoods strategies and social/kinship networks in Peru in times of crisis. Secondly, our case study shows how much these flows were, although shock-induced, manifestations of traditionally fluid peasant mobilities that have long been practiced in rural life, reflecting reticular livelihoods among interwoven family networks across urban and rural areas in Peru. Rather than conceptualisations as ‘reversal’ or ‘return migrations’, the findings therefore argue for their understanding as ‘mobilities’ to reflect their multi-spatiality, the reticularity and historically networked construction of rural livelihoods strategies in the Andes, surfacing in times of crisis.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".