The Influence of Legislation and Regulation on Strategy in Public Entities
Bibliographic record
Abstract
In terms of prescriptions contained in the Constitution of the Republic of South Africa, 1996, citizens are entitled to, inter alia, the provision of services in a sustainable manner.Citizens are also entitled to the promotion of social and economic development to meet their basic needs.Government designs systems and processes to meet those needs in response to policy goals and objectives as defined in legislation and regulation in the context of the principalagent approach.Similarly, strategy is needed to devise techniques and plans to meet needs, goals and aspirations of government in the most efficient manner.It is premised on leadership, goal orientation and satisfying a multitude of factors in the process.At face value it would seem that the enactment of certain legislation and regulations appear to render the need for strategy obsolete, especially since there is a proliferation of national, provincial and local policies and strategies that only need implementation.This article reviews the influence of legislation and regulations on strategy in public entities, focusing on a development corporation in the Eastern Cape as a case study.For purposes of data collection a mixed-methods research methodology approach was followed.The article concludes with a proposed normative model to enhance strategy in public entities.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".