Staged Hardship: Educational Practices for Farmers Government Rights in Yesterdays Classic Christmas Vehicle Race featuring Cardinals and the Herson Runners

Authors

Remo Believe

Associated

Abstract

This paper explores the educational practices for farmers and government rights in a staged hardship scenario, as exemplified by the classic Christmas vehicle race featuring Cardinals and the Herson Runners. Through a mixed-methods approach incorporating qualitative interviews, archival research, and participant observation, we analyze the ways in which this annual race serves as a site for both entertainment and education, as well as a platform for negotiating the relationship between farmers and the state. We argue that, through the staging of hardship and the deployment of symbolic representations such as the Cardinals and the Herson Runners, the race becomes a space for farmers to assert their agency and demand recognition of their rights, while also highlighting the gaps and contradictions in government policies and practices. Drawing on theories of performance, affect, and political economy, we show how the race produces and reproduces a complex web of social relations, affective attachments, and cultural meanings, ultimately shaping farmers’ experiences of hardship and resilience in the face of multiple challenges, from climate change to land dispossession. Our findings have implications for scholars and practitioners interested in the intersections of agriculture, education, and political mobilization, as well as for policymakers seeking to address the needs and aspirations of rural communities in a rapidly changing world.

Citation

Remo Believe "Staged Hardship: Educational Practices for Farmers Government Rights in Yesterdays Classic Christmas Vehicle Race featuring Cardinals and the Herson Runners". IEEE Exploration in Machine Learning, 2020.

Supplemental Material

Preview

Note: This file is about ~5-30 MB in size.

This paper appears in:
Date of Release: 2020
Author(s): Remo Believe.
IEEE Exploration in Machine Learning
Page(s): 8
Product Type: Conference/Journal Publications