From policy coherence to 21st century convergence: a whole‐of‐society paradigm of human and economic development
Bibliographic record
Abstract
The 20th century saw accelerated human and economic development, with increased convergence in income, wealth, and living standards around the world. For a large part, owing to the well-entrenched Western-centric linear and siloed industrialization pattern, this positive transformation has also been associated with complex societal challenges at the nexus of agricultural, industrial, and health sectors. Efforts at cross-sectoral policy coherence have been deployed with limited success. To go beyond what has been possible thus far, the whole-of-society (WoS) paradigm for human and economic development proposes a 21st century convergence where, instead of the rest (of the world) converging with the West, sectoral and cross-sectoral efforts converge in their single and collective policy and action on a common target of human and economic development. In this paper, we first review and discuss contributions and limitations of policy coherence approaches. We then elaborate the institutional foundation of the WoS paradigm, taking as an anchor the well-established model of polycentric governance that views individuals, and state, market, and community, forming society as part of the same complex adaptive system. Actors within such systems self-organize into nested hierarchies that operate at multiple scales and move toward 21st century convergence of human and economic development.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".