Bibliographic record
Abstract
In this exchange, the composer and director Thuthuka Sibisi shares the artistic research he has developed based on portraits of members of a nineteenth-century South African choir. The original photographs were found in the Hulton archives in London, one hundred and twenty-five years after The African (Native) Choir toured Great Britain, Canada and the United States. Sibisi has retraced the journey of these fourteen portraits through the visual and sound installation, The African Choir 1891 Re-imagined (2014-2016), produced in collaboration with Renée Mussai and Philip Miller. He has continued his research on images with choreographer and dancer Gregory Maqoma. Broken Chord is the fruit of this new collaboration. For this show, he set the bodies of sixteen dancers in motion by infusing Xhosa and Zulu sounds, rhythms and words into the scores of Bach, Rossini and Purcell.In this interview, Thuthuka Sibisi describes the links he weaves between the trajectories of the singers of the African Choir, whose silent images remain, and his own personal experience. He describes his research process as a sinuous path that dialectises the contemporary iteration of the contradictions that bind black bodies, simultaneously and paradoxically, exposed and suppressed.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".