Mitigating Developmental Disparities and Regional Instability through Public Policy Landscaping
Bibliographic record
Abstract
Since government is the universally-accepted system that is responsible for midwifing the development and progress of nations and governance is mainly delivered through the making and implementation of public policy, it is imperative to develop a strategic policy view of the root-cause(s) of the bad governance that triggers the developmental disparities within and between nations which invariably engender national and regional instability across many corners of the globe. Developmental disparities within and between nations are especially important as the primary causes of national and regional conflicts as well as trans-national migration and sundry trans-national crimes such as human trafficking. In this article, I argue that public policy is so central to governance and pivotal to national development and progress that it must be recognized as the powerful force that can either unite polities around the pursuit of development and progress or leave them deeply-divided and starved of much-needed development. The crux of this argument is that national development and progress are impossible without national unity and regional stability. My second argument is that commitment to the practice of policy-led governance should be considered doubtful unless it can be proven by the prioritization of the institutionalization of Public Policy systems that are designed to, first and foremost, foster national unity and regional stability. The mission of this article is to introduce the novel concept of Public Policy Landscaping as the strategic means of making the governance landscape suitable for the development and deployment of the environmentally-sensitive public policy systems that can be relied upon to unite policy-led entities around the pursuit of national and regional development. Public policy does to the governance what landscape architecture does to natural land and this means that any neglect of public policy landscaping is bound to leave the governance landscape in a poor state that will invariably impede good policymaking and policy implementation.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".