The Cosmic Horizon as the Highest Heaven: Examining Michael Guillen's Hypothesis

Belay Sitotaw Goshu — 2026-03-04

The human tendency to locate the divine in spatial terms represents a cognitive universal, yet classical theism affirms divine transcendence, the claim that God as Creator cannot be contained within creation. Into this tension steps physicist Michael Guillen, who has proposed that the "highest heaven" described in biblical tradition possesses a specific physical location: the cosmic horizon, the boundary of the observable universe where galaxies recede at the speed of light and time ceases. This article critically examines Guillen's hypothesis, evaluating whether it successfully integrates scientific cosmology with theological claims about divine transcendence and the nature of heaven. The study employs interdisciplinary analysis, drawing on cosmological literature to clarify the scientific status of the cosmic horizon, and on theological sources (biblical, patristic, medieval, and contemporary) to assess the hypothesis against classical and modern understandings of divine transcendence, immanence, and eschatology. The analysis proceeds through comparative evaluation of scientific and theological frameworks. While Guillen's hypothesis has received popular attention, sustained scholarly analysis remains lacking. This article provides the first systematic examination of the proposal within the science-religion literature, demonstrating how the hypothesis reveals fundamental tensions between spatial models of divinity and theological commitments to transcendence. It offers a constructive alternative: interpreting the cosmic horizon as a symbol of transcendence rather than a literal location. Scientifically, Guillen misconstrues the cosmic horizon, which is not a physical location but an observer-dependent observational boundary. Theologically, his spatial literalism conflicts with classical theism's affirmation that God cannot be contained within creation (1 Kings 8:27; Augustine; Aquinas).

The Augmented Science Journalist: A Human-in-the-Loop Framework for AI Integration

Belay Sitotaw Goshu — 2026-03-06

Algorithmic bias in healthcare systems has emerged as a critical threat to equitable patient care, with growing evidence that machine learning models perpetuate racial and ethnic disparities in clinical decision-making. This study aimed to investigate the extent, evolution, and real-world consequences of bias in healthcare algorithms through an innovative human-in-the-loop (HITL) investigative journalism framework. The methodology integrated AI-driven discovery, automated code repository auditing, and in-depth human investigation across three phases. AI tools analyzed temporal bias trends from 2015–2023, audited over 50 public GitHub repositories, and quantified disparities, while human journalists conducted expert interviews, impact assessments, and narrative synthesis to ensure contextual accuracy and ethical framing. Key findings revealed persistent and severe biases: Black and Native American patients experienced 2–3 times higher bias scores than White patients, with diagnostic and risk-prediction algorithms showing the greatest disparities. Only 33% of analyzed repositories included explicit bias testing, despite high adoption rates. Consequential impacts included false negative rates up to 73.7% for Black patients needing care, elevated treatment disparities, poorer health outcomes, and substantial economic costs from excess hospitalizations. The novelty lies in the scalable HITL synergy that enabled longitudinal, multi-source analysis previously infeasible manually, translating technical artifacts into actionable public knowledge. In conclusion, unchecked algorithmic bias systematically harms marginalized communities. We recommend mandatory bias audits, regulatory oversight of proprietary systems, and participatory governance involving affected patients.

Konfrontasi: Jurnal Kultural, Ekonomi dan Perubahan Sosial

The Cosmic Horizon as the Highest Heaven: Examining Michael Guillen's Hypothesis

The Augmented Science Journalist: A Human-in-the-Loop Framework for AI Integration