A comparative analysis of the roles and functions of the Inspector-General of intelligence with specific reference to South Africa
Bibliographic record
Abstract
The dissertation conducts a comparative analysis of the roles of the Inspectors-General of Intelligence with specific reference to South Africa. The analysis assessed the roles, functions and structures of the office of the Inspectors-General in the following countries: Canada, Australia, New Zealand, the United States of America, South Africa and equivalent institutions in the United Kingdom. The study was based on a review of existing literature and interviews and written responses with some of the members of the Joint Standing Committee on Intelligence, the former Minister for Intelligence Services, LN Sisulu, the head of the intelligence division of the South African National Defence Force, the former deputy Director-General of the South African Secret Service, judge Gordon who is responsible for interception of communications and the current Inspector-General of Intelligence, Mr ZT Ngcakani. The performance of the office of the Inspector General of Intelligence since 1995 indicates that: (a) there were ambiguities in the legal framework for the office of the Inspector-General which led to various interpretations by stakeholders on the functioning of the office; (b) there is still a need to test the impact of the office of the Inspector General on the Services and the public; and (c) the Inspector-General's office requires other oversight mechanisms to complement its functions.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".