Bibliographic record
Abstract
This article offers an examination of Lee Hirsch’s Amandla!: A Revolution in Four-Part Harmony. Beginning with the liberation songs that gained salience during the National Party’s implementation of apartheid policy in 1948 and ending with the struggle songs of a post-1994 democratic South Africa, the documentary’s aim is to retrieve and recount the role of freedom songs in antiapartheid struggle. Using the writings of Ernesto Laclau, John Mbiti, Paul Ricoeur, and Alfred Schutz, this essay will argue that liberation songs are ancestral text that were partly used by antiapartheid activists to create their collective identities. This essay will further argue that Amandla! set itself the task of retrieving South Africa’s liberation songs and liberation’s praise singers from the ancestral region John Mbiti calls Zamani to a region he calls Sasa. However, this essay will assert that the ancestral retrieval task of this documentary is compromised by the documentary’s privileging of the hegemonic groups within the African National Congress (ANC), the documentary’s presentation of the ANC as a monolithic and univocal organization, and the producer’s snowball sampling method. Arguing that this documentary relegates some of the South African struggle experiences into Zamani, this essay will attempt to correct these omissions and broaden the context of liberation songs.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".