Contribution à l’évaluation des dispositifs de gouvernance et d’évaluation de la qualité dans l’enseignement supérieur mozambicain
Bibliographic record
Abstract
Over the past twenty years, higher education systems have been strongly affected by the development of quality paradigm. This phenomenon is combined with new concerns like external and internal evaluations, quality assurance, accreditation and ranking. Similarly, debates about universities’ mission increase in so far as they are evaluated compared with what they offer. Nowadays, in a context marked out by resources scarcity, competition and various requirements that stand in front of academic institutions, such as diversification of the supply of training connected to the market requirements, entrepreneurship and improvement of quality, universities leaders, teachers-researchers and different actors of governance seem to be obliged to increase their learning organization strategies approaches. Although university is a place of production, a place of diffusion and a place of knowledge conservation above all, few researches have explicitly questioned the learning contribution in the strategic positioning of academic institutions. In this thesis, the author tries to approach university governance from the point of view of knowledge management. Following a both inductive and comparative processes, this research is focused on the analysis of the main schemes of quality evaluation in Mozambican higher education system.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".