Funding, ideology and the aesthetics of the development film in postcolonial Zimbabwe

This article considers the relationships between aesthetics and ideology in donor-funded ‘development’ film-making from Zimbabwe, examining in particular how the films’ producers have attempted to popularize a genre of film-making that has its roots in colonial cinema. Making close reference to two productions from the Harare-based Media for Development Trust (MFD) – Neria (Godwin Mawaru, 1992), and Everyone’s Child (Tsitsi Dangarembga, 1996) (both of which may be regarded as archetypal examples of their genre) – the article demonstrates how the films deploy a range of aesthetic strategies to imbue a set of narratives drawn from colonial development films with greater impact and cultural resonance for contemporary local audiences. The article also suggests that close analysis of these strategies may provide insights into the relationships between the films’ aesthetic dimensions and wider ideological issues in the region.

DRAKULA HALÁLA (1921): The Cinema's First Dracula.

This essay covers the history of Károly Lajthay’s Hungarian film Drakula halála (1921), the cinema’s first adaptation of Bram Stoker’s novel Dracula. The essay attempts to construct a production history of the film, as well as to create an accurate list of cast members and key filming locations. As Drakula halála is lost, the essay also features the very first English translation of an extremely rare 1924 Hungarian novella based on the film, which offers much insight into its narrative.

Inferring and Exploiting Categories for Next Location Prediction

Predicting the next location of a user based on their previous visiting pattern is one of the primary tasks over data from location based social networks (LBSNs) such as Foursquare. Many different aspects of these so-called “check-in” profiles of a user have been made use of in this task, including spatial and temporal information of check-ins as well as the social network information of the user. Building more sophisticated prediction models by enriching these check-in data by combining them with information from other sources is challenging due to the limited data that these LBSNs expose due to privacy concerns. In this paper, we propose a framework to use the location data from LBSNs, combine it with the data from maps for associating a set of venue categories with these locations. For example, if the user is found to be checking in at a mall that has cafes, cinemas and restaurants according to the map, all these information is associated. This category information is then leveraged to predict the next checkin location by the user. Our experiments with publicly available check-in dataset show that this approach improves on the state-of-the-art methods for location prediction.