The financial impacts of carry overs in the budgetary and financial implementation of the IFES of the Central-West region in the period 2008 to 2016
Bibliographic record
Abstract
The object of study of this research is expenses classified as carryovers, which are part of the public budget, and they are a challenge for public managers. The objective of the research is to propose a control instrument applicable to the budgetary and financial execution of an IFES, considering the legislation applicable to the public budget, that assists the manager in controlling and reducing the effects of expenses classified as carryovers. Descriptive analysis and application of Pearson's correlation index were carried out on data taken from the Federal Government's Portal, referring to the five universities in the Center-West region between 2008 and 2016. It is concluded that: carryovers are present in all universities evaluated; on average, more than a quarter of the budget started is not finalized in the same year; unprocessed leftovers account for more than 90% of the total unpaid debts of universities. Furthermore, it is found that: there is a moderate negative correlation to budget execution and unbundling of UNB; there is a moderate positive correlation between the remainders to be paid and the financial execution of the budget in the UFGD and the UNB, and negative correlation in the UFG. The intervention proposal has two stages: the first one is the collection of information on the budgetary and financial situation of IFES, through a control sheet, and the second step is the application of the GUT matrix in the budget balances of the carryovers, to prioritize the closure of such expenses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".