Utilization of information technology: An effective means of public investment management at autonomous universities in Vietnam considering the Covid-19 pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Covid-19 has been causing a large-scale pandemic, along with the rapid development of information technology. Information technology means such as digital applications will be one of the effective tools to help manage public investment and investment in general in Vietnam. Public investment management at autonomous universities in Vietnam is in the final stage of completion in terms of procedures, legal documents, and implementation. This study was designed to shed light on the factors that affect the management of public investments at Vietnamese autonomous universities. The study's data was gathered from a survey of 126 public investment managers at autonomous universities in Vietnam. Factor analysis and multivariate regression methods were used to analyze the influence of factors on public investment management at autonomous universities in Vietnam. According to research findings, the capacity of public investment management agencies at autonomous universities; Distribution of funds for the implementation of public investment projects; administrative procedures, legal provisions, and the actual context affect public investment management at autonomous universities in Vietnam. With Standardized Coefficients = 0.482, distribution of funds for implementation of public investment projects at schools at autonomous universities were the most influential factors. The research results are the foundation for proposing solutions to improve the efficiency of public investment management at these universities in Vietnam such as promulgating legal documents, building public investment management processes suitable to the characteristics of universities, developing, and proactively implementing works in investment management and operating results of public investment in autonomous University.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it