The Tasharuk Framework: A Rigorous Systems Engineering Model for Community-Funded, AI-Augmented Peer Review in Open Access Publishing

Conventional peer review paradigms in scholarly publishing are encumbered by systemic inefficiencies, such as extended review durations, prohibitive article processing charges (APCs), and vulnerability to cognitive and institutional biases, which collectively hinder equitable dissemination of scientific knowledge \citep{tomkins2017reviewer, stelmakh2023citation}. This theoretical manuscript delineates the \textbf{Tasharuk Framework}, an integrative systems engineering construct that amalgamates incentivized human-AI hybrid adjudication, preemptive community-based crowdfunding, and Bayesian frameworks for uncertainty propagation to enhance operational efficiency, methodological integrity, and procedural transparency. The framework is rigorously formalized via a tripartite protocol: (i) monetarily compensated peer adjudication informed by principal-agent utility maximization; (ii) AI-mediated integrity assessment leveraging probabilistic graphical models; and (iii) iterative open community validation phases. Financial viability is secured through a stochastic lock-and-release funding mechanism, with Monte Carlo simulations (10,000 iterations) demonstrating a 92.95% attainment probability for a $400 threshold (95% CI: [92.3%, 93.6%]). Empirical substantiation encompasses sensitivity analysis utilizing correlation-based proxies to approximate first-order effects (\( \lambda_d \approx 0.53 \), \( \mu_d \approx 0.57 \), \( \tau \approx -0.25 \)) and total-order effects (\( ST_{\lambda_d} \approx 0.64 \), \( ST_{\mu_d} \approx 0.70 \), \( ST_{\tau} \approx 0.35 \)), with interaction approximation \( S_{\lambda_d \mu_d} \approx 0.12 \); Bayesian posterior estimation of acceptance rates (posterior means: 0.60, 0.80, 0.40; 95% credible intervals: [(0.19, 0.93), (0.40, 0.99), (0.07, 0.81)]), and Popperian falsifiability evaluations (null likelihood ratios: 0.375, 0.125, 0.375, with aggregated \( -2\ln\Lambda \approx 8.08 > \chi^2_{1,0.05} = 3.84 \)). All derivations are underpinned by executable Python simulations, ensuring full reproducibility and empirical falsifiability. The Tasharuk Framework reconceptualizes scholarly publishing as a participatory epistemic commons, harmonizing velocity, veracity, and equity in a post-APC landscape, while proactively mitigating emergent risks of AI-induced artifacts in evaluative processes \citep{naddaf2025ai}.学术出版领域的传统同行评审范式存在系统性效率缺陷，诸如评审周期冗长、高昂的文章处理费（Article Processing Charges, APCs）以及易受认知与制度性偏见影响等问题，这些弊端共同阻碍了科学知识的公平传播[tomkins2017reviewer, stelmakh2023citation]。本理论性稿件阐述了**塔沙鲁克框架（Tasharuk Framework）**，这是一种集成化系统工程架构，融合了激励式人机混合评审、前置性社区众筹以及用于不确定性传播的贝叶斯框架，旨在提升运营效率、方法学严谨性与流程透明度。该框架通过一套三方协议得到严格形式化：(i) 基于委托-代理效用最大化原则的有偿同行评审；(ii) 借助概率图模型开展的AI辅助严谨性评估；(iii) 迭代式开放社区验证阶段。该框架通过随机锁仓发放资助机制保障财务可持续性；蒙特卡洛模拟（Monte Carlo Simulations, 10000次迭代）结果显示，针对400美元阈值的达成概率为92.95%（95%置信区间（Confidence Interval, CI）：[92.3%, 93.6%]）。实证验证涵盖：采用基于相关性的代理变量近似一阶效应（$ lambda_d approx 0.53 $, $ mu_d approx 0.57 $, $ au approx -0.25 $）与总阶效应（$ ST_{lambda_d} approx 0.64 $, $ ST_{mu_d} approx 0.70 $, $ ST_{ au} approx 0.35 $）的敏感性分析，以及交互效应近似值 $ S_{lambda_d mu_d} approx 0.12 $；针对录用率的贝叶斯后验估计（后验均值：0.60、0.80、0.40；95%可信区间（Credible Interval, CI）：[(0.19, 0.93), (0.40, 0.99), (0.07, 0.81)]）；以及波普尔式可证伪性评估（原假设似然比：0.375、0.125、0.375，合并统计量 $ -2lnLambda approx 8.08 > chi^2_{1,0.05} = 3.84 $）。所有推导均基于可执行的Python模拟代码，确保了结果的完全可复现性与实证可证伪性。**塔沙鲁克框架**将学术出版重新概念化为一种参与式认知公共域，在后文章处理费时代协调速度、严谨性与公平性，同时主动规避评审过程中AI生成伪影的潜在风险[naddaf2025ai]。