The Legal and Political Framework for Archaeology and the Protection of Archaeological Resources in South Africa
Bibliographic record
Abstract
Abstract It is no surprise that the legal framework that protects archaeological and other heritage resources in South Africa is firmly rooted in the country’s political history and latterly in internationally accepted guidelines. The British colonial system that was applied in many African colonies in the 20th century, for example Zimbabwe (Southern Rhodesia), Botswana (Bechuanaland), and Tanzania (Tanganyika), shaped the early legislation and, until the new millennium, was essentially reactive. Western-style government was firmly in charge, traditional managers were not consulted, and legal action could be taken (but seldom was) against those who ignored the protective measures and damaged the archaeological material or site. In South Africa, the National Heritage Resources Act (Act 25 of 1999), which was implemented by the new democratically elected government in 2000, six years after the fall of apartheid, broadened the range of definitions to identify mainly historical places of significance that had not been recorded before, such as sites of slavery and graves of victims of political conflict. Proactive measures were introduced to assess the impact of development on archaeological sites and their mitigation before development, and the assessment process guides management strategies to retain the significance. Some of these reforms were borrowed from legislation in former British colonies such as Australia, New Zealand, and Canada, and the framework was influenced by international guidelines such as the Burra Charter and the Operational Guidelines for the World Heritage Convention. The experience that has been gained since 2000, particularly through the involvement of the public at the local level, has highlighted issues for legislative review that will pay more attention to traditional management, skills development, monitoring, and local government responsibilities, than to policing. The aim is to enable the public to protect archaeological and other heritage resources because they are significant to them and not only because there is a law that prohibits their destruction without a permit. Successful implementation will continue to depend on the political value that these resources are perceived to have in a country where historical places of the 20th century generally have more heritage interest than archaeology.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.041 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".