An Assessment of the Applicability of Behavioral Economics’ Tools to Policy Making Process Considering Sustainable Development Goals
Bibliographic record
Abstract
Achieving sustainable development goals in a very dynamic and complicated world requires innovated solutions. As people are in the heart of the developmental process, understanding what motivates people and what drives their behaviors is a must for designing policies targeting the achievement of developmental goals. This paper aims to assess how the behavioral economics’ tools may be applied to directing people’s behaviors toward more sustainable activities and then contributing to achieve sustainable development goals. Using deductive qualitative approach, and a comparative analysis, the study explores and discusses to what extent insights and techniques from behavioral economics may affect and change policy making process and then public policies' outcomes specifically in the context of sustainable development disciplines. The results showed a vital role of behavioral economics tools in developing public policies in accordance to real behaviors of people which -in turn- help in achieving sustainable development goals. Moreover, it was concluded that changing humans' behaviors toward more sustainable patterns of life provides so many opportunities to strengthen the effectiveness of policies for sustainable development in both developed and developing countries. Using behavioral economics tools, policymakers can design more effective policies to achieve Sustainable Development Goals (SDGs).
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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 | 0.061 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".