The Global Jukebox: A public database of performing arts and culture

Abstract

Standardized cross-cultural databases of the arts are critical to a balanced scientific understanding of the performing arts, and their role in other domains of human society. This paper introduces the Global Jukebox as a resource for comparative and cross-cultural study of the performing arts and culture. The Global Jukebox adds an extensive and detailed global database of the performing arts that enlarges our understanding of human cultural diversity. Initially prototyped by Alan Lomax in the 1980s, its core is the Cantometrics dataset, encompassing standardized codings on 37 aspects of musical style for 5,776 traditional songs from 1,026 societies. The Cantometrics dataset has been cleaned and checked for reliability and accuracy, and includes a full coding guide with audio training examples (https://theglobaljukebox.org/?songsofearth). Also being released are seven additional datasets coding and describing instrumentation, conversation, popular music, vowel and consonant placement, breath management, social factors, and societies. For the first time, all digitized Global Jukebox data are being made available in open-access, downloadable format (https://github.com/theglobaljukebox), linked with streaming audio recordings (theglobaljukebox.org) to the maximum extent allowed while respecting copyright and the wishes of culture-bearers. The data are cross-indexed with the Database of Peoples, Languages, and Cultures (D-PLACE) to allow researchers to test hypotheses about worldwide coevolution of aesthetic patterns and traditions. As an example, we analyze the global relationship between song style and societal complexity, showing that they are robustly related, in contrast to previous critiques claiming that these proposed relationships were an artifact of autocorrelation (though causal mechanisms remain unresolved).

Fig 1. Global map of 10 song-style regions previously identified by factor analyses of Cantometrics data (adapted from [15]).

Lomax and Arensberg’s novel methods, execution, and conclusions drew considerable criticism when they were published and subsequently (e.g., [–36]; cf. [14,17,18] for review and discussion). One major impediment to overcoming criticism was that the raw Cantometric data and sample were never made public for others to examine and reanalyze. The public release of the Global Jukebox now largely solves this problem.

Fig 2. Screenshot of metadata and Cantometric codings of the Grenadian song “In My Own Native Land” recorded in 1962 by Alan Lomax and ALCW.

Fig 3. An example map showing the global distribution of modal codings for one of the 37 Cantometric variables: CV23 (“Embellishment”).

Embellishment is a technique in which rapid, ephemeral notes ornament the main melodic line, but are distinct from it. The distribution of highly embellished singing (in red tones) outlines the”Silk Road” region of Eurasian cultural exchange which includes the Mediterranean, North Africa, the Arabian Peninsula, the Middle East, Western Asia, South Asia, Southeast Asia, and East Asia. This highly embellished singing is differentiated from Eastern and Central Europe, sub-Saharan Africa, Oceania, and the Americas where singing is mostly unembellished (blue tones).

Fig 4. A comparison of n = 135 societies with fully matched data for five Cantometric variables of musical style and seven Ethnographic Atlas variables of social complexity.

a) A map of the global distribution of the first principal component (PC1; interpreted as “musical differentiation”) for five musical variables from the Global Jukebox’s Cantometrics dataset (Musical organization of the Orchestra (CV7), Text repetition (CV10), Embellishment (CV23), Melodic interval size, and Enunciation (CV37). b) a) A map of the global distribution of the PC1 (interpreted as “societal complexity”) for six social variables from D-PLACE’s Ethnographic Atlas dataset (Jurisdictional hierarchy beyond the local community (EA033), Subsistence (an aggregate variable described in the SM); Class (EA066), Caste (EA068), Slavery (EA070), and Community size (EA031). c) The correlation between musical PC1 and social PC1 was significant when controlling for possible autocorrelation using linguistic relatedness and geographic proximity (see Supplementary Material S1.9 in S1 File for modeling details and analyses of bivariate correlations between the musical and social variables).