Education for Learning to Live Together: what can we learn from the South African experience?
Bibliographic record
Abstract
In 1994, South Africa moved away from its cruel and divided past to a future where its citizens would learn together, work together and grow together. In short we had to learn what it meant to live together by unlearning the ideas introduced by apartheid that permeated every aspect of our society. This required a new Constitution, brave and exemplary leadership by Nelson Mandela and others and a Truth and Reconciliation Commission led by Desmond Tutu. None of these efforts, as important as they may have been, could ever be sufficient to sustain change. Ordinary people who have no positional authority are those who will sustain change. Roughly one quarter of the South African population is at school and these are people who will take the message of reconciliation into the future. In this article we describe attempts to redefine what is good. In particular what kind of teacher, learner and curriculum we will need to form the basis of a transformed and admirable society, one in which we will know how to live together.
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.035 |
| Scholarly communication | 0.016 | 0.033 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 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".