Creating learning and action space in South Africa’s post-apartheid land redistribution program
Bibliographic record
Abstract
This paper uses the case of South Africa’s latest land redistribution strategy known as the Proactive Land Acquisition Strategy, to explore whether, and how, research can have direct and positive impacts on beneficiaries of land reform. The study is situated within the practice of action research: to explore how it can generate knowledge that can be shared back and forth between stakeholders, as well as how it may ignite changes that the participants desire. The findings are that Proactive Land Acquisition Strategy is not meeting the overall goals land reform. But action research has allowed the beneficiaries to emerge from the process with new knowledge about their rights, as well as what options they have to move forward in their fight for secure land rights and decent livelihoods. We introduce a concept of a ‘learning and action space’ to explain our practice of action research. The paper concludes that action research is a desirable approach for land reform, but while it succeeded in educating beneficiaries, it is only one ingredient in ongoing struggles to challenge power relations among citizens and between citizens and the state.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.041 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.004 | 0.005 |
| 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".