Awareness and Implementation of Automated Accounting System for a State University in Pangasinan, Philippines
Bibliographic record
Abstract
The study proposed a cost-effective automated accounting system for a State University in Pangasinan, applicable to other higher education institutions. Using a mixed-method research design, the study collected both qualitative and quantitative data through a Focus Group Discussion with key university officials from the accounting department and a survey of 100 accounting personnel across nine campuses, an open university system, and a school of advanced studies during the fourth quarter of 2023. Additionally, the study revisited various legal issuances and related documents to perform a comparative cost-effectiveness analysis between the existing manual system and the proposed automated accounting system. Findings revealed that, although the accounting personnel are generally aware of the current processes, there is a moderately serious problem with the existing system, particularly regarding its features. The cost analysis showed potential savings of P1,052,950.56 over three years with automation. The study recommends implementing the proposed automated accounting system to improve efficiency and cost-effectiveness, which could serve as a model for other higher education institutions.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".