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, protracted review latencies, prohibitive article-processing charges (APCs), and susceptibility to cognitive and institutional bias, that collectively impede equitable dissemination of scientific knowledge. This theoretical and computational manuscript formalizes the Tasharuk Framework, a systems-engineering construct that couples incentivized human AI hybrid adjudication, pre-emptive community crowdfunding, and Bayesian uncertainty propagation into a single procedural architecture. The framework is specified through a tripartite protocol: (i) monetarily compensated peer adjudication grounded in principal agent utility maximization; (ii) AI-mediated integrity vetting via a probabilistic graphical model; and (iii) a time-bounded open community-challenge phase. Financial viability is examined through a compound Poisson Exponential funding model, validated by two independent routes, a 200,000-path Monte Carlo simulation and an exact analytical (Poisson-mixed Gamma) computation, which agree to within 0.07 percentage points and jointly estimate the probability of reaching a $400 funding threshold at P(F≥τ) = 92.65% (95% CI [92.54%, 92.77%]; analytical value 92.73%). Global sensitivity is derived using a from-scratch Saltelli (2002) double-loop Sobol estimator with bootstrap confidence intervals, attributing first-order variance shares of Sλd = 0.253, Sμd = 0.335, Sτ = 0.059 to donor arrival rate, per-donor contribution, and funding threshold respectively, with a non-trivial interaction share of 35.2% indicating that the two dominant parameters cannot be tuned independently. Reviewer-level Bayesian posterior estimation (Beta Binomial updating) and a Wilks likelihood-ratio falsifiability test against the null θ0 = 0.5 jointly reject the null hypothesis of non-informative adjudication (−2 ln Λ ≈ 8.08 > χ²1,0.05 = 3.84, p = 0.0045). A parameter-uncertainty propagation analysis further shows that realistic ±15% calibration uncertainty in donor behavior widens the attainment probability to a 90% interval of [18.8%, 100.0%], materially qualifying the framework's headline viability claim. All reported figures are produced by, and reproducible from, the executable Python code embedded in this manuscript (Appendix A); no numerical claim in the text was computed by hand. The manuscript further specifies an explicit scientific and technical risk register, a falsifiability-anchored experimental validation roadmap, and a critical (rather than purely tabular) comparison against existing crowdfunding and hybrid peer-review literature, positioning Tasharuk as a testable, risk-aware proposal for a post-APC publishing commons rather than a finished operational system.