A Systems Thinking Framework for Assessing and Enhancing Jordan’s Progress Towards SDG 16.1
Why this work is in the frame
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Bibliographic record
Abstract
This study was initially designed to assess Jordan’s decade-long progress toward sustainable development goal (SDG) 16.1. However, that assessment revealed a puzzling ‘Jordanian paradox’, which necessitated the development of a systems-thinking framework to explain the data and pinpoint leverage points for accelerated violence reduction. Drawing on 11 years (2013–2023) of official police and socioeconomic data series, we combine descriptive statistics, trend lines, Pearson correlations (with a focus on |r| > 0.80) and high-fit (R2 > 0.75) regression models. The data show that while violent and property offenses fell significantly, drug-trafficking and cybercrime surged. Two feedback structures explain this paradox. A balancing loop shows that conventional policing suppresses traditional violence. A reinforcing loop links high unemployment and broader socioeconomic strain to modern, networked offenses that erode public trust, deter investment, and feed back into unemployment. A policy-ready causal loop diagram (CLD) visualizes these dynamics. Scenario modeling indicates that cutting unemployment by two percentage points could prevent approximately 1990 drug cases and 2200 cyber incidents annually, while producing only a modest uptick in crimes against persons. Sustaining SDG 16.1 gains, therefore, requires an integrated package that simultaneously weakens the reinforcing loop – through youth jobs, social protection and digital-literacy programs – and strengthens the balancing loop via targeted cyber- and border-security measures.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it