The Production and Consumption of Music in the Digital Age. Edited by B.J.Hracs, M.Seman, and T.E.Virani, New York, NY: Routledge. 2016. 278 pp, $115.00 (hardback edition). ISBN 9781‐13885‐1658.
Bibliographic record
Abstract
Music geography has enjoyed significant theoretical and empirical advances since the 1980s, in part due to emerging digital technologies and the spatial impact of globalization forces.Music has always been inextricably intertwined with the constantly shifting cultural, economic, political, and social relationships that shape who we are, what we produce, and how we consume.Music reflects social values, hopes, and aspirations, and serves as a mirror for societies in myriad spatial settings.This welcome volume brings scholarship on music firmly into the new millennium, with the goal of explicating recent transformations in the production and consumption of music across today's rapidly changing marketplace.In an introductory overview, the volume's editors contextualize the following 17 chapters by arguing for an economic geography framework that shows how an "interconnected sonic ecosystem" (p. 3) is emerging at multiple scales and across diverse musical landscapes.Collections of essays are notoriously difficult sometimes to link together with consistent themes and approaches.The editors divided the contributions into five sections: recording, working, playing, distributing, and promoting and consuming.At first glance, this approach does not seem appropriate, as distribution would logically follow recording, while working and playing should, perhaps, be at the end, with production and consumption positioned in the middle of the five sections.This is a minor quibble, though, as the individual essays do fit well within each section and there is a coherent interconnecting structure to each essay and to each section.A larger concern is the noticeable absence of maps and graphics, with the exception of the very engaging chapter on musical venues in Pittsburgh and Nashville.Chapter 8, for example, examines the local music scene in Dalston, London, with no visual evidence at all of the place, context, culture, or built environment.Essays like this one cry out for visual support to show the where, how, and why of music consumption.Two essays in the recording section (Chapters 2 and 3) offer empirical evidence of the challenges facing music production in a digital world.At the micro-level, Watson examines freelance music producers and their working conditions within a framework created by the significant mobility and plasticity of contemporary digital recording technologies.His argument is that this type of labor leads to a merging of regular work hours with leisure time, something he calls labor "extensification," as well as an increase in demand for the producers' time in a 24/7 production environment, which Watson describes as the "intensification" (p.18) of labor demand.Ardita argues that, at the macro-scale, the dispersal of recording studio locations away from traditional central-city sites in London, New York, and Los Angeles, for example, driven by a wave of new digital audio workstations, has disrupted traditional labor relationships.Ever-cheaper recording equipment has opened up the industry to competition by circumventing traditional but costly recording studios.This chapter hit home for me because my younger brother in the UK has a very successful business designing and constructing Growth and Change
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".