The effect of public governance, human resource quality, characteristics of the government internal su-pervisory apparatus, and the government internal supervisory system on the quality of local government financial report
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
The objective of this research is to study and analyze the direct effect of public governance, human resource competence, characteristics of Government Internal Supervisory Apparatus, and Government Internal Supervisory System on the quality of Local Government Financial Report and the indirect effect of public governance, human resource competence, and characteristics of Government Internal Supervisory Apparatus on the quality of Local Government Financial Report with the mediation of Government Internal Supervisory System. The samples are 148 local government agencies’ financial administration officials, selected according to certain criteria. As SEM-PLS was used for hypothesis testing, this study finds that public governance, human resource competence, characteristics of Government Internal Supervisory Apparatus, and Government Internal Supervisory System directly have positive effects on the quality of Local Government Financial Report. Furthermore, the Government Internal Supervisory System fully mediates the effect of human resource competence on the quality of the Local Government Financial Report and partially mediates the effects of public governance and characteristics of Government Internal Supervisory Apparatus on Local Government Financial Report.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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 it