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| ai.viXra.org > Social Science > 2512 |
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[2] ai.viXra.org:2512.0055 [pdf] submitted on 2025-12-14 21:23:02
Authors: Brent Hartshorn
Comments: 3 Pages.
Traditional canine programs in schools rely on expensive, highly trained security or therapy dogs, creating models that are costly and difficult to scale, particularly in the public school sector. This paper proposes the Cohort Canine Model (CCM), an alternative to the prevailing high-cost, law-enforcement-centric approach. We assert that while advanced training is effective, a cost-prohibitive requirement for all schools, a more sustainable model can be achieved by leveraging the natural protective instincts and therapeutic value of domesticated dogs. Our proposal centers on integrating young Pitbull-type dogs as classroom pets in lower grades, who then travel with the student cohort through primary and secondary school. By replacing the handler and training costs with natural bonding and cohort loyalty, we estimate the annual per-canine cost can be dramatically reduced, allowing for widespread adoption. This model shifts the focus from aggressive deterrence to pervasive psychological security and rapport-building within the school community.
Category: Social Science
[1] ai.viXra.org:2512.0049 [pdf] submitted on 2025-12-12 21:47:44
Authors: Philipp D. Dubach
Comments: 5 Pages.
We present an empirical analysis of collective attention dynamics on Hacker News, a technology-focused social news platform with over 18 years of continuous operation. Using a dataset of 98,586 items with 22,457 temporal snapshots collected during December 2025, we examine attention decay patterns, preferential attachment mechanisms, content survival, and the predictive power of early engagement metrics. Our analysis reveals: (1) attention decay follows a power law with exponent α= 0.56(R2 = 0.73), indicating slower-than-exponential decline; (2) extreme attention inequality with a Gini coefficient of 0.91, yet absent preferential attachment (ρ=−0.04); and(3) early velocity strongly predicts final success (ρ= 0.74, p < 10−100) with 97.6% precision for viral content identification. These results contribute to our understandingof how online communities allocate attention and have implications for platform design and content recommendation systems.
Category: Social Science