Performance Management System in Mozambican Universities: A Literature Review of Theories, Origin and Evolution
Bibliographic record
Abstract
From the end of the 1970s up to the 2000s, governments in the developed and developing countries were involved in implementing economic, social, political, cultural and legal reform programs. The first wave of public sector reforms came under the Structural Adjustment Programs (SAPs) which were implemented in most of the developing countries from the late 1970s to the early 1990s. The second wave, which started in the early 1990s, was propelled by influence generated by proponents of the New Public Management (NPM) school of thought. The major objective of reforms was to enhance performance and productivity in public sector organizations including higher education institutions. This practice was grounded on certain theories and models, mainly public-choice theory and goal-setting theory under the New Public Management (NPM) model. The Government of Mozambique has adopted a performance-based approach to implementing public sector reforms. This study, which employs a qualitative literature survey with secondary data as its primary research source, discusses and analyzes literature on the design and implementation of a Performance Management System (PMS) in the public sector including public universities of Mozambique. The study also discusses the origins and evolution of the theories which are linked to PMS and their applicability to the public universities of Mozambique as they started embracing PMS as a tool for improving performance of individuals and the organization as a whole.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".