Evaluation of Critical Factors Influencing Framework Agreement in Public Procurement: Evidence from Administrative Offices in Ethiopia
Bibliographic record
Abstract
The purpose of this study is to determine the factors influencing framework agreement in public procurement in the case of Siltie Zone Administrative offices, Ethiopia. To achieve this objective, this study used explanatory research design and quantitative research approach. The study used a survey questionnaire as a tool for data collection with the use of census sampling technique and applied multiple linear regression analysis for the data analysis. The results of the study found that corruption and organizational factors have a significant positive impact on framework agreement. On the other hand, the social factors found to be insignificant in the study. By looking at the influence of independent variables on framework agreement, the study ensures the core issues in public procurement offices which affect the government budget. The researchers recommend that government needs to work on the troubled agency involved in the wastage of a large portion of the government budget. The study adds to the current body of knowledge in framework agreement research. Furthermore, concerned bodies can utilize the findings of this study as a guide to comprehend the significance of framework agreement in taking appropriate measures.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".