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| Main Authors: | , , , , , , |
|---|---|
| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2025
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| Subjects: | |
| Online Access: | https://doi.org/10.5281/zenodo.18091831 |
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Table of Contents:
- <p><span>This article operationalizes Islamic rapprochement as a governed, measurable program. It fuses Causal Layered Analysis litany, systems, discourse, myth with a KPI-driven performance architecture (UCTA-PA) comprising 280 indicators: 140 Crisis Warning Indicators (CWIs) and 140 Strategic Progress Indicators (SPIs). A mixed-methods design integrates AI-assisted corpus and network analytics, purposive–stratified expert input, institutional document analysis, and pre-registered quasi-experimental estimators (staggered difference-in-differences, synthetic control, interrupted time-series). Programmatic validation shows target-concordant movements: reductions in Hate-Speech Rate and Polarization Velocity; increases in the Theological Respect Index and Joint-Institution Density; improvements in legal Interoperability Score; and severity-weighted declines in Inter-sect Incident Rate. Mechanism checks indicate discourse improvements precede incident reductions under high implementation fidelity. Theoretically, the study extends CLA by specifying a governance-grade indicator grammar that projects deep discursive and mythic drivers onto observable signals. Practically, it delivers a thermostat for decision-makers traffic-light thresholds tied to corrective playbooks, explicit ownership, and cadences enabling auditable progress toward rapprochement within a 12–24-month horizon. Actionable recommendations include publishing an indicator dictionary with non-compensatory floors for CWIs, aligning financing and recognition to verified SPI gains, and embedding narrative telemetry in data-governance pipelines with bias auditing and privacy-by-design.</span></p>