Like Cattle for Slaughter? Reading Nervous Conditions ' Pedagogical Interventions
Bibliographic record
Abstract
In this paper, Tsitsi Dangarembga's Nervous Conditions is approached as both a novel about education and as a creative work of pedagogical theory. Its motivating questions are: can Nervous Conditions contribute to discussions of pedagogical practice? What are its criticisms of conventional models of education, and what might it recommend in their stead? The novel's educational narrative is read alongside the critical theories of Paulo Freire, Henry Giroux and bell hooks, and their interplay offers a new perspective from which to assess Dangarembga's pedagogical vision. Drawing heavily on textual detail, this paper identifies five major tenets of this vision – Learning as forgetting; The rule of engagement; 'Serving' dominant interests; Critical literacy; and Community, not competition – which form the basis of a liberatory pedagogy based on principles of social responsibility, community involvement, personal engagement, and anti-oppressive action.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".