Difficulties in the Application of Accounting and Management Control in Higher Education Institutions in Portugal
Bibliographic record
Abstract
Approximately two decades after the approval of POCP, and following an assessment of the need for an accounting system that meets the demands of proper planning, accountability, and financial control, the SNC-AP was introduced. This system, regulated by 27 Public Accounting Standards, has faced challenges in its implementation. Therefore, it is relevant to analyze how managers of Portuguese higher education institutions (HEIs) perceive this issue. The objective of this research is to determine whether HEI managers use management control tools, which management control models are adopted, and the difficulties encountered in their implementation. To achieve this, a qualitative empirical study was conducted through semi-structured interviews with 12 administrators and financial directors from Portuguese higher education institutions (HEIs). The results show that management accounting is complex and challenging to implement. Portuguese HEIs are still in the early stages of adopting these tools, with progress limited to defining activities and cost centers. Conditions have not yet been established to calculate, for example, the cost per course, student, project, or service, as outlined in NCP27 of the SNC-AP.
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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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".