Journalistic authority is not purely a newsroom product but a governed outcome: editorial judgment, platform algorithms and regulatory rules jointly shape what citizens see, trust and can hold to account, creating a 'Contested Epistemic Space' with tangible implications for platform economics and policy.

Main Finding

Journalism’s epistemic authority is not solely produced inside newsrooms; it is a governed outcome that emerges from the interaction of three interdependent sociotechnical logics—editorial, algorithmic, and regulatory. Their overlap creates a "Contested Epistemic Space" where visibility, credibility, and accountability are negotiated and stabilized (or destabilized).

Key Points

• Three interdependent logics shape public knowledge:
  • Editorial: traditional newsroom practices, professional judgment, gatekeeping, editorial norms.
  • Algorithmic: platform ranking, recommendation, personalization, and the technical affordances that structure visibility and distribution.
  • Regulatory: laws, rules, and oversight that impose obligations, safeguards, and accountability mechanisms.
• These logics are best understood as sociotechnical arrangements (drawing on Actor-Network Theory) that include human actors, institutions, and material/technical artifacts.
• The intersection of the three logics forms a Contested Epistemic Space where:
  • Visibility (what people see) is negotiated between editorial choices and algorithmic amplification.
  • Credibility (what people trust) is shaped by editorial standards, algorithmic signals, and regulatory legitimacy.
  • Accountability (who is answerable) is distributed across journalists, platforms, and regulators.
• The framework integrates literatures that previously treated editorial, algorithmic, and regulatory domains separately, offering a lens to study how authority is assembled, contested, and sometimes stabilized.

Data & Methods

• The article is primarily theoretical and conceptual, developing the "Epistemic Governance Framework."
• The methodological foundation draws on Actor-Network Theory to treat logics as networks of actors (human and non-human) and their relations.
• The contribution is an analytical scaffolding rather than an empirical study; it outlines how empirical work could operationalize and test the framework (e.g., tracing actor-networks, analyzing platform affordances, mapping regulatory interventions).
• Suggested empirical directions implicit in the framework include mixed methods: qualitative case studies of governance interventions, network analyses of distribution chains, algorithmic auditing, and comparative regulatory analysis.

Implications for AI Economics

• Platform economics and attention markets:
  • Algorithmic logics determine attention allocation; economic models must incorporate how ranking and recommendation create distributional externalities for news producers and influence demand for different content types.
  • Competition for visibility shapes incentives (e.g., sensationalism, engagement-optimized content) with welfare implications for information quality.
• Incentives and market structure:
  • Editorial incentives (resource constraints, revenue models) interact with algorithmic signals to determine supply of journalism. Platform fee structures, ad markets, and subscription dynamics will influence newsroom viability.
  • Platform market power over distribution can create bargaining and monopoly dynamics; regulatory interventions (e.g., content-moderation mandates, data-sharing or interoperability rules, platform liability) alter these strategic environments.
• Externalities and public goods:
  • High-quality journalism has public-good characteristics; algorithmic amplification of low-quality content generates negative externalities. Policy design needs to internalize these externalities (subsidies, platform payments, antitrust remedies).
• Regulation and policy design:
  • Regulations that target platforms (transparency, auditability, algorithmic impact assessments) change the cost-benefit calculus for algorithmic design and can shift equilibrium outcomes in news markets.
  • Policymakers should consider second-order effects: e.g., forcing algorithmic transparency might alter strategic behavior by platforms and producers in unforeseen ways.
• Empirical and modeling priorities for AI economists:
  • Quantify how algorithmic ranking affects news firm revenues, audience reach, and content quality.
  • Model multi-actor equilibria that include newsroom editorial choice, platform algorithmic design, and regulatory constraints.
  • Evaluate welfare impacts of governance interventions (content moderation rules, platform payments to news, algorithmic audits) using causal inference and structural models.
  • Study distributional effects across population segments and geographic markets; regulatory changes can have heterogeneous effects.
• Normative and institutional considerations:
  • Governance of public knowledge requires mechanisms that align private incentives (platforms, newsrooms) with social welfare. Economic policy tools should be informed by the sociotechnical nature of epistemic authority emphasized in the framework.
  • Interventions should be assessed not only on efficiency grounds but on their effects on credibility and accountability in public information ecosystems.

If you want, I can convert this framework into specific empirical research designs or sketch structural/equilibrium models that capture the three-logics interaction and policy counterfactuals.