"May the unfixable broken bone/ ⦠give us new bearings": Ethics, Affect and Irresolution in Ingrid de Kok's A Room Full of Questions
Bibliographic record
Abstract
This article makes the case, by way of readings of Ingrid de Kok's A room full of questions, a sequence of 12 poems that respond to South Africa's Truth and Reconciliation hearings, that it is particularly important in the wake of the critical industry generated by the TRC process to return to artistic engagements with the process. De Kok's poems strikingly resist three familiar tropes which inform much of the rhetoric surrounding the TRC and the liberal media's celebration of the rainbow nation, and which underpin even some of the most sophisticated critical engagements with the Commission's work: epistemological tropes of revelation and transparency, organicist tropes of healing and recovery, and economic tropes of account-settling. In refusing to concede to the logics of any of these tropes, the poems confront readers very directly with our own desires for, expectations of, and investments in projects indexed towards achieving understanding, healing, and reconciliation, our own assumptions about the role of language and narrative as vehicles of social transformation. Because the poems do not fulfill any of the expectations or grant any of the consolations offered by these tropes, and in fact stage breakdowns in the processes upon which they are predicated, readers are invited into the space of ethical encounter and encouraged to consider how other paths towards transformation might begin to be carved out.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.077 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".