Bibliographic record
Abstract
“Good governance” as an issue of development is being widely used for improvements in socio-economic outcomes and for aid effectiveness and considered as the effective tool for overcoming multidimensional challenges existed in both developed as well as developing countries of the world and it has generated increasing attention and debate both at the national and international level over the past two decades . The concept of ‘good governance’ conveys the qualitative dimension of governance that indicates effective, efficient, participative, or democratic form of government which is responsible for transparent and accountable management of human, natural, economic and financial resources for equitable and sustainable development. Addition of the adjective ‘good’ to governance has given a sense of enhancement and almost become an obsession in the recent debates on international development and public administration in developing countries. Other than the nations, international organizations such as the World Bank, the United Nations Development Programme (UNDP), Asian Development Bank (ADB), the Organization for Economic Co-operation and Development (OECD), the European Union (EU) and other donor agencies, have given rigorous importance to the issue of governance – particularly for aid receiving countries. The purpose of this article is to develop a conceptual framework on governance and good governance. This article highlights the emergence of governance as a shifting paradigm from government along with the differences between government and governance. It also focuses the meaning of governance and good governance in general and particularly the views from World Bank and UNDP as a problem solving mechanisms. For ensuring effective performance of the institutions, different international organizations like World Bank and UNDP addressed good governance indicators which are also explore in this article. Keywords : Governance, Good Governance, Theoretical Framework
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.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".