The effect of automated information systems on the Kenyan county government’s operations: A case study of Kiambu county government
Bibliographic record
Abstract
Accounting plays a very crucial role in the management and success or failure of most organizations.As a system, automated information system records and processes data of transaction and events into useful information for use in planning, controlling and operation of businesses.Kenya is a growing economy, and the global pressure has forced it to embrace E-Governance practices.As a result, ways to improve their operations have been placed.This study aimed at establishing the effect of implementing Automated Information Systems (AIS) on the County Government's operations.The County operations were classified in terms of transparency and record keeping as well as supervision while the AIS was operationalized in terms of the Zizi System, the County Pro System and the Integrated Financial Management Information System (IFMIS).This study employed complete enumeration survey method to collect data from all the twelve sub-counties in Kiambu, Kenya.The respondents were the IT managers, Financial Officers, and the Revenue Officers.Multiple regression was used to test the effect of AIS on the Kenya County Governments' operation.The findings of this study indicate that implementing the Automated Information Systems had a significant positive effect on the county government's operations..
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".