A Tale of Two Crises in Peru: Livelihoods and Social Reproduction During the 1980s and the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has wreaked havoc on the livelihoods ofpeople around the world. Structural economic constraints are highlightedat such moments of crisis, while those most affected have recourse to theirrepertoire of managing strategies. This case study of people from Allpachico,a Peruvian peasant community, compares their responses to the current crisiswith their responses to one in the 1980s, showcasing similarities in strategies(especially reciprocity and the sale or exchange of necessary reproductive tasksand products) and differences in the form they take. In the 1980s, women’s workand kin reciprocity helped people access use-values. By 2020, neoliberalismhad transformed the national economy and Allpachiqueño migrantsoverwhelmingly had precarious informal and contract work. Reciprocity andreproductive tasks are still central to livelihood, but now tend to be monetizedrather than involving use-values. As that earlier crisis shattered both secureemployment and peasant farming to lay the basis for neoliberalism, so now itappears that the COVID-19 pandemic, through the monetization of governmentsupport and reciprocity alike, is accelerating financialization in the form offinancial services and debt.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".