Implementation and Challenges of Administrative Assistants in the Utilization of Maintenance and Other Operating Expenses (MOOE)
Bibliographic record
Abstract
Abstract: Administrative Assistants play a critical role in supporting school heads with managing school finances, including the allocation and utilization of the Maintenance and Other Operating Expenses (MOOE) fund. This study sought to determine the extent of implementation and challenges of administrative assistants in the utilization of MOOE in the Division of Northern Negros during the second quarter of the calendar year of 2024. Using a mixed-method research design, seventy (70) administrative assistants were surveyed while ten (10) were interviewed. Quantitative analysis there is a moderate implementation of administrative assistants utilizing MOOE in management and monitoring, particularly among those with the highest educational attainment. There was a significant difference primarily in procurement and liquidation with younger, lower educational backgrounds and shorter length of services. Meanwhile, thematic analysis revealed that administrative assistants encountered challenges such as insufficient budget allocation, fund realignment, liquidation difficulty, market price fluctuations, logistical issues, unplanned expenses, limited material availability, payment problems, communication gaps, inconsistent policies, centralized decision-making, overburdened staff, and inadequate training complicate MOOE fund management in schools. The findings imply that there is a need for improvements and support for the administrative assistants to function effectively. It is essential to provide ongoing training, technical assistance, and other professional development opportunities for administrative assistants who focus on procurement processes, accounting procedures, and financial planning. Keywords: Administrative Assistants, Maintenance and Other Operating Expenses (MOOE), Implementation, Challenges, Procurement and Liquidation, Realignment, Division of Northern Negros
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".