The Impact of Crime against a Person on Domestic Investment in Dubai
Bibliographic record
Abstract
The rise in crime against a person in rapidly growing cities poses significant risks to societies and economies, affecting both microeconomic and macroeconomic aspects. This trend could potentially deter economic performance and domestic investment. Consequently, this study aims to analyze the impact of crime against a person on domestic investment in Dubai spanning 1989–2021. Dubai is considered an emerging economy and a highly competitive global city. It is also acknowledged as one of the world’s smart cities. This study employed the novel nonlinear autoregressive distributed lag (NARDL) approach to investigate the impact of both the escalation and contraction of crime against a person on domestic investment in Dubai. The findings exhibit that the fluctuation in crime against a person has an asymmetrical impact on domestic investment. In addition, estimations of the positive and negative long-run asymmetric coefficients indicate that crime against a person has a negative impact on domestic investment in Dubai.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".