The Incorporation of the fpic Principle in South African Policy on Mining-Induced Displacements
Bibliographic record
Abstract
Abstract The persistence of intense conflicts over land belonging to indigenous communities is a pressing issue in South Africa. This would resonate even more with the lived experiences of mine communities that are often having to grapple with the collateral socio-economic hardships and impoverishment effects. Matters can get even worse when the State has weak legal and institutional frameworks to regulate the use and possession of such highly contested lands. This paper explores the principle of free, prior and informed consent (fpic); its relevance and significance to South African extractive sector; its possible bottlenecks and implementation challenges; and suggestions for practical interventions and policy advances to facilitate its incorporation into law and policy. Displacement is the limited context within which this exploration is undertaken. The paper argues that the fpic – as an envisaged future for the country’s extractive policy – is more desirable than a mere duty to consult as currently framed in the empowering legislation namely, the Minerals and Petroleum Resources Act (mprda).
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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 | 0.073 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.014 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".