Introduction: Reconfiguring Precarious Landscapes: The Road Movie in Latin America

Abstract

Forty minutes into Walter Salles’s Diarios de motocicleta (2004), the bikers Ernesto Guevara (Gael García Bernal) and Alberto Granados (Rodrigo de la Serna) bump into a herd of cows while traveling an unpaved Chilean road and wreck their already unreliable motorcycle, which they have ironically named La Poderosa (the mighty one). An editing cut takes viewers to the back of a truck that transports the protagonists and their broken motorcycle to Los Ángeles , the nearest town with a repair shop. They share the space with a cow and two indigenous men, a Mapuche father (Juan Maliqueo) and his son (Samuel Cifuentes), who are conversing in Mapudungun. The camera pans to show us Ernesto’s fascination with the pair of indigenous men, the first of a number of indigenous peoples they will encounter during their journey. A cut to a close-up of the cow’s face from Ernesto’s point of view is followed by Ernesto’s attempt to reciprocate the generosity of the locals who are giving them a ride by offering his expert medical opinion: “That cow’s going blind.” The son replies with indifference to Ernesto’s diagnosis and surprises both Ernesto and the viewers by making a nonchalant observation that is nonetheless replete with social critique: “All she’s going to see is shit.” Although seemingly inconsequential in the broader context of the film, this brief scene encapsulates several distinctive features that make Latin American road movies unique: the tense relationship of Latin American countries with modernity as epitomized by the precarious infrastructures and the uneven access to motorized vehicles and other modern technological advances; and the use of nonprofessional actors, shooting on location, and natural lighting as neorealist techniques to showcase such tough realities of the region as persistent poverty, class differences, and marginalization of indigenous populations. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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