Creanalytics: Automating the supercut as a form of critical technical practice

Abstract

Based on the notion of critical technical practice and its resonances with recent debates in digital film and media scholarship, in this article I outline a type of computational practice that aims to couple the relational-analytic powers of machine learning with the explanatory-creative powers of visual narrative. I provisionally call this approach creanalytics. To enact this coupling, I designed a system to annotate and classify a large corpus of film clips, automatically extract fragments from this corpus, and edit them into new compositions, rendered into a computational supercut, which I go on to argue can be understood as the minimal expression of a broader emergent form of media: the computational video essay. Below I describe the most salient technical aspects of this system, analyse the principles of its design, and discuss the methodological and conceptual possibilities of its use as a format that mediates between critics and their networked environments, and between individual media artefacts, their parts, and the larger collections to which they belong.

Pluralising critical technical practice

“…In the articles in this subsection of ‘Reflexive Making’, AI is not just employed, but also questioned through its application. Daniel Chávez Heras's (2023) article ‘ Creanalytics : Automating the supercut as a form of critical technical practice' offers insight into the prototype of a computational system that automates the supercut, a cinematic technique. Through the application of ML techniques, it produces short compilation videos from a large dataset of film clips.…”

“…Ogden et al (2023), Rieder et al (2022), and Chao et al (2024) in this special issue), in order to access and assess their ‘cascades of inscriptions’ (Ruppert et al, 2013), creating awareness for their workings, their glitches, and flaws. This can be done through active and deliberate application of the underlying technologies and infrastructures, including AI and specifically ML techniques (Chávez Heras (2023) and Sánchez Querubín and Niederer (2022) in this issue) as well as through computational tool(-)making (Chao et al (2024) and Rieder et al (2022) in this issue).…”