Steering the Poverty‐Environment Nexus in Central Asia: A metagovernance analysis of the Poverty‐Environment Initiative (<scp>PEI</scp>)
Bibliographic record
Abstract
Abstract The close and reciprocal ties between poverty and environmental degradation present significant potential for simultaneous improvement of the livelihood of the poorest along with increased opportunities and enhanced resilience of the environment and natural resources. By supporting governments and other stakeholders in designing and implementing development plans that tackle environmental and poverty concerns in a joint manner, the globally operatingUNDP‐UNEPPoverty‐Environment Initiative (PEI) addresses a major governance challenge for sustainable development (SD) and the Sustainable Development Goals (SDGs) in particular. Focusing on Central Asia, and specifically Tajikistan, a country outside the spotlight of studies concerned withSDgovernance mechanisms, and through the analysis ofPEIprogramme documents and stakeholders’ interviews, this article probes into the governance and governance co‐ordination (metagovernance) settings forSD. The article closes by presenting a set of recommendations to improve governance co‐ordination, while achieving more inclusive decision‐making and ultimately increasing the impact ofPEIon the society and the environment. Specifically, it argues for improved information policy and enhanced integration of endogenous knowledge. Furthermore, national and local development planning and private initiatives should be better linked, and the different levels of governance for poverty‐environment mainstreaming should be more coherent. The solutions discussed are of relevance for wider Central Asia and the global community engaged in moving theSDGs into the mainstream of governance and policy frameworks.
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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 | 0.039 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".