Die glansende skadu van haar afwesigheid : die logika van supplementariteit in André P. Brink se Donkermaan
Bibliographic record
Abstract
In Donkermaan (2000), Ruben Olivier's narrative is characterised not only by a very pessimistic portrayal of the socio-political realities of contemporary South Africa, but also by a disconsolate depiction of a personal life slowly coming undone. In short succession Ruben loses his wife, Riana, his job as librarian and suffers an angina attack. To make matters worse, one of his sons has left the country, while the other is about to emigrate to Canada, and his best friend and neighbour, Johnny MacFarlane, is brutally murdered in his own home. On a rainy April evening, an enigmatic stranger, Tessa Butler, appears on his doorstep. To Ruben she represents the watershed between a problematic past (permeated with acute feelings of loss and guilt) and a promising future (a second chance). This article examines the crucial role Tessa plays in Ruben's life and narrative. The striking similarities between Ruben and Tessa's relationship and that between Jean-Jacques Rousseau and Madame de Warens (as recounted by Rousseau in his Confessions, 1712-1778) are explored. The logic of supplementarity that Derrida identifies with regard to the writings of Rousseau, is subsequently applied to Ruben's narrative. Emphasis is placed on the fact that Ruben regards Tessa as a supplement for loved ones lost and that she therefore represents a chance for him to compensate for past mistakes and failures. In conclusion it becomes clear that Ruben's narrative is not an unimaginative imitation of Derrida's logic of supplementarity, but deviates from it in one important respect.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".