Bibliographic record
Abstract
Abstract The term “signal flow” was introduced as an engineering metaphor in the 1920s to articulate the relationalities of audio signals when selecting pieces of equipment and sound in the context of recording, mixing, and mastering. Despite the widespread use of digital audio workstations in mainstream music production and audio engineering today, students in the recording arts still must learn and understand signal flows through practical exercises and discursive repetition. This is particularly true for marginalized recordists tasked with proving themselves performatively and professionally, as they continually navigate biased assessments of their foundational knowledge, technical and musical capacities, and finished work. In this chapter, the authors extend a metaphorical usage of “signal flow” to an examination of the career paths of women and gender non-conforming (WGNC) producers and engineers in a strongly male-dominated and discriminative industry. Based on the experience and vision of eight WGNC recordists, educators, and audio program organizers located in Canada at the time of the study, the authors outline how alternative educational “flows” create paths that bypass the direct line of heteropatriarchal norms and conventions. Also, to troubleshoot habitual silences and stoppages, this chapter offers feminist-informed pedagogical models that better support and sustain early-career WGNC recordists, contributing to much-needed structural changes in the music industry.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.097 | 0.017 |
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".