Book Review: The Science-Music Borderlands: Reckoning with the past and imagining the future / Edited by Elizabeth H. Margulis, Psyche Loui, and Deirdre Loughridge. 2023, MIT Press, 387 pages.
Bibliographic record
Abstract
The Science-Music Borderlands is a new critical contribution to the field of interdisciplinary music research, promising intellectual synthesis of cutting-edge music research that has been enabled by the convening power of the Society for Music Perception and Cognition (SMPC). Served primarily by North American constituencies, this international organisation represents the intellectual and professional interests of an international music science community. While the majority of contributors are affiliated with Universities and elite research institutions in the USA (18) and Canada (6), the volume editors (Northeastern University and Princeton, USA) have involved numerous contributions from Europe and the UK (11) and East Asia (4); institutional affiliations also feature from India, Australia, New Zealand, Nigeria and Republic of South Africa. The geographical range is important, for reasons that the editors themselves foreground: that “despite the unique opportunities for confluence afforded by the more than century-long existence of humanistic and scientific inquiry into music, and despite the potential offered by the decades-long existence of a society [SMPC] that strives to foster interdisciplinary collaboration, rifts between the approaches persist… [yet] researchers have been steadily working toward new paradigms informed by developments across disciplinary boundaries and the global conditions of the twenty-first century.” (p.2). The institutional and global conditions in which music research takes place matter a great deal. The influence of politics and policy at such wider levels facilitates - and constrains – both the knowledge conditions and the practical opportunities that are available to access music’s multi-disciplinary expressions.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.041 |
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".