Achieving Sustainability Goals in Central Asia: The Importance of “the Middle”
Bibliographic record
Abstract
As the world passes the midpoint of the 2030 United Nations Sustainable Development Goals (SDGs), clarity about the implementation of this agenda becomes increasingly critical. Equally important is the extent to which the SDG framework is succeeding or failing in different contexts. This study explores the role and importance of the “implementers”, mid-level actors that bridge policymaking between national leaderships who set policy and grassroots efforts. Focusing on three SDGs (#2, 6 and 15), thirty (30) such implementers were selected through purposive sampling combined with subsequent reputational strategies. The knowledge and attitudes of these actors toward the goals and progress of their respective nations were evaluated through structured interviews. While the general outlook on their countries’ capacity to reach the proposed goals was positive, the detailed review of SDG targets showed signs that those in “the middle” were highly uncertain as to whether these goals could be reached. Considering the critical role of these implementers in translating policy to action, this creates serious concern about the path forward in sustainable development moving toward 2030 and beyond. Moreover, critical reconsideration of the process of implementing the SDGs needs to be undertaken to capitalize on the expertise and strategic capacity of “the middle” of sustainable 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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".