FROM TRANSCODING TO ADAPTATION: AN AI-ASSISTED MEDIATION MODEL FOR DIGITAL MEDIA DISCOURSE
FROM TRANSCODING TO ADAPTATION: AN AI-ASSISTED MEDIATION MODEL FOR DIGITAL MEDIA DISCOURSE

Summary/Abstract: In the context of global information flows, generic machine translations often result in “semantic flattening” and increased informational entropy. This issue is critical in media translation, where complex socio-legal terms lose their relevance through literal transcoding. The present research proposes and evaluates the Semiotic Governance Protocol v3.0.14, a hybrid algorithmic intercultural mediation model designed to transform the raw textual output of Artificial Intelligence into a high-prestige informational product (see APPENDIX A). The central objective is to demonstrate how hybrid algorithmic mediation ensures the “survival of meaning.” The study employs a comparative methodology. Datasets of news articles (Case study: justice reform/vetting) were analyzed and translated using two methods: (1) generic prompting (transcoding) and (2) the application of Protocol v3.0.14 based on the GASP Audit reasoning (Governance, Accuracy, Semiotics, Pragmatics). Empirical analysis reveals that the application of the protocol eliminates the “contextual blindness” of algorithms. The results demonstrate a consistent improvement in performance indicators: the restoration of discursive authority (e.g., transitioning to Active Voice), terminological precision through functional anchoring, and strategic framing of headlines to maximize relevance according to Relevance Theory (Wilson & Sperber). The research validates the model of critical symbiosis, demonstrating that the future of media translation lies not in total automation, but in hybrid algorithmic mediation. The proposed protocol provides newsrooms with a rigorous tool to maintain “digital truth” and discursive authority in the face of risks such as disinformation and algorithmic simplification.

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