Resource-Gated Prediction: How Favor, Funding, and Institutional Access Reshape Identity and Project Design

Resource-Gated Prediction (RGP) is proposed as an extension of the Belonging–Energy–Prediction framework. It describes a field-dependent process in which access to financial, institutional, relational, reputational, or opportunity-based resources becomes conditional on alignment with a resource-bearing gatekeeper, causing an individual, organization, or project to optimize toward anticipated approval rather than evidence, internally authored values, beneficiary needs, or original mission. The framework introduces resource-access error, defined as the perceived mismatch between an agent’s current configuration and the configuration expected to preserve access to a valued resource. RGP treats conditional access as a field condition connecting Belonging to eligibility, Energy to the capacity to tolerate rejection, and Prediction to the inferred preferences of the gatekeeper. A formal model introduces a gatekeeper-weight parameter determined by resource dependence, belonging threat, criterion opacity, regulatory Energy, and accessible alternatives. The model distinguishes four regimes: Translation, Calibration, Predictive Capture, and Mission Inversion. It derives two central propositions: equilibrium reference drift depends on both gatekeeper weight and gatekeeper–reference divergence, while identity rigidity slows convergence without changing the fixed point when gatekeeper weight is exogenous. When accessible alternatives are made endogenous, specialization toward one gatekeeper can reduce legibility to other fields, producing a self-reinforcing dependence spiral, bistability, and capture hysteresis. The paper also distinguishes actual, perceived, and internalized gatekeeper pressure and presents ten falsifiable predictions, representative experimental paradigms, a provisional measurement instrument, a case-analysis protocol, and institutional safeguards. This is a theoretical preprint. No dedicated empirical test has yet been conducted. Model parameters are illustrative rather than empirically estimated, and the accompanying simulation demonstrates internal coherence rather than empirical adequacy. Code and figures: https://github.com/BEPframework/rgp-modelSeries: BEP Principle SeriesStatus: Preprint v1.0, not peer reviewed.