Bibliographic record
Abstract
In the last fifty years, Congolese popular dance music (also known as “Congolese rumba”) has become something of a musica franca for much of sub-Saharan Africa. As Congolese like to say, the captivating sound of their music, firmly grounded in Africa’s encounter with afro-Cuban culture, has “colonized the rest of the continent”, but the music has gone through a series of important aesthetic changes since it first emerged in the urban colonial centers of the Belgian Congo. Despite this rich history, limited research has been done on the subject and very little has been published on Congolese popular music from the point of view of aesthetics. By tuning in on local conversations about certain aspects of the music’s structure and form, this text attempts to understand how Congolese popular dance music attempts to transcend the ugliness of an ongoing political and economic crisis that has become increasingly acute since independence (la conjoncture) and how this particular expression of beauty enables us to better understand the relationship between aesthetics and politics more generally. The perception of “noise” in the analysis of popular music reveals more about our inability to understand non-Western aesthetic criteria than about popular music per se. Drawing from the philosophical hermeneutics of Hans-Georg Gadamer, the metaphor of listening is used to argue that the difficulty of hearing music from someone else’s point of view should not be used to justify a retreat into the self.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".