A Proposed Model in Fundamental of Decision Making Process at Local Government Level
Bibliographic record
Abstract
Currently we are witnessing significant changes in the nature of policy making and management in local government. Essentially, these changes represent a move away from the relatively passive approach towards a more purposeful approach in which local authorities attempt to learn about the nature and causes of local problems and to respond accordingly. Therefore, what is required is a theoretical framework that could explain and relates the concept of planning to the concept of power, and seeks an explanation of decision-making process. The organizational changes and complex interaction in local government that involve politicians, planners, officers and decision-making bring about the important of current practices and evaluating future changes. Thus, this article tries to propose a model of decision making-process for greater performance in local government. In the findings, there are a few factors influencing councilors in running their functions. It shows that councillor as representatives at local government level will be a good policy maker in handling local manners by adopting this proposed model. In future research, to be a greater practice of rational decision making, perhaps the stakeholders such as households, business communities, community leaders and Non-Governmental Organizations (NGOs) will be collaborated effectively with councillor in decision making-process relates to local affairs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".