Active Listening: The Cultural Politics of Magnetic Recording Technologies in North America, 1945-1993.
Bibliographic record
Abstract
From the late 1940s to the mid 1990s, the use of magnetic tape recorders provoked aesthetic, social, and political debates about the decentralization of sonic production. At the very moment that postwar mass culture seemed most ascendant and critics began to identify it as a coherent object of study and scorn, reel-to-reel tape recorders allowed users to reproduce and manipulate mass-produced sounds emanating from radio and recording studios, as well as the sounds of their households, their communities, and the larger world outside their homes. Many non-professional tape users, non-commercial sonic researchers, and hobbyist audio networkers would come to believe that they could be more than passive recipients of culture industry products and the dominant ideologies that they transmitted; through an active engagement with tape, they hoped to teach listeners to become producers themselves. Listening to their works produced via tape, reading their voluminous writings, and combing their archival collections for evidence of wider connections to their practices, I argue that such tape enthusiasts developed a set of media theories through a self-reflexive recording practice I call active listening. This dissertation follows hobbyists and professional recordists ranging from New York City folklorist and advertiser Tony Schwartz, composer and educator R. Murray Schafer and his World Soundscape Project in Vancouver, British Columbia, and the Iowa City-based audio collective the Tape-beatles, who all proposed multiple forms of engagement with, against, and about mass culture. They made structural critiques of commercial culture industries for separating producers from consumers in the name of profits, perceptual arguments about the capacity for sound to activate new political imaginaries, and aesthetic moves that aimed to reintegrate presumably alienated listening subjects. Not only did the ubiquity of mass culture throughout North America give listeners a shared vocabulary, but the act of appropriating and manipulating sounds on tape fostered a self-consciousness about how mass culture worked and how it might be made to work differently. Such forms of engagement both attempted to eliminate boundaries between the production and consumption of mass culture and bolstered an ideological investment in the idea of mass culture as a passive and alienating force.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".