The Open Artist Elevation Protocol: A Distributed Framework for Detecting and Amplifying Cultural Emergence

The Open Artist Elevation Protocol: A Distributed Framework for Detecting and Amplifying Cultural Emergence

DOI: (to be assigned)

John Swygert

March 22, 2026

Abstract

This paper introduces the Open Artist Elevation Protocol (AEP), a distributed and replicable framework for identifying and amplifying emerging artists within large-scale digital ecosystems. The protocol addresses the authority gap identified in prior work by replacing centralized selection with structured, transparent, and measurable processes. Rather than relying on subjective curation or algorithmic popularity alone, AEP integrates multi-dimensional engagement signals and coherence filtering to detect non-random emergence. Within TSTOEAO, this process is interpreted as a form of constraint-driven selection, though the protocol remains fully functional independent of that interpretation. The Swygert Equilibrium Quotient (SEQ) is used as a ranking metric for stability and repeatability of artist growth patterns. The protocol is designed to be executed by individuals or distributed networks without requiring institutional control.

  1. Introduction

The absence of effective artist elevation mechanisms in modern platforms necessitates a new approach. The Open Artist Elevation Protocol is designed to be:

  • transparent

  • replicable

  • decentralized

It provides a structured method for identifying artists whose growth reflects genuine audience resonance rather than artificial amplification.

  1. Signal Acquisition

The protocol begins with the collection of engagement data across platforms. Relevant inputs include:

  • repeat listens

  • audience retention

  • share velocity

  • comment depth

  • cross-platform consistency

These signals prioritize quality of engagement over raw volume.

  1. Coherence Filtering

Raw engagement data is filtered to identify patterns that indicate stability and non-random growth. This includes:

  • sustained engagement over time

  • consistency across independent audiences

  • resistance to rapid decay

This stage removes noise and isolates meaningful emergence.

  1. Emergence Detection

Artists meeting coherence criteria are flagged as candidates for elevation. This stage identifies:

  • growth trajectories that deviate from random distribution

  • clustering of engagement patterns

  • early-stage convergence of attention

  1. Commitment Phase

The protocol introduces a critical step absent in current platforms:

A deliberate commitment to selected artists.

Participants in the network collectively choose to:

  • amplify exposure

  • share content

  • support continued growth

This transforms detection into action.

  1. Distributed Amplification

Instead of centralized promotion, amplification occurs through a network of independent participants. This creates:

  • organic growth

  • resilience to manipulation

  • alignment with audience behavior

  1. SEQ Integration

The Swygert Equilibrium Quotient (SEQ) is used to rank candidate artists based on:

  • stability of engagement

  • repeatability of growth

  • structural coherence

Higher SEQ values correspond to more reliable emergence patterns.

  1. Falsifiability

The protocol is weakened if:

  • identified artists fail to achieve sustained growth

  • results cannot be replicated across independent groups

It gains support if:

  • multiple independent networks identify the same artists

  • selected artists achieve measurable and sustained elevation

Conclusion

The Open Artist Elevation Protocol provides a practical solution to the authority collapse in digital music. By combining structured detection with distributed commitment, it enables the emergence of culturally significant artists without centralized control. The protocol is immediately implementable and scalable across platforms.

References

Swygert, John. “Failure of Platform-Based Artist Elevation.” Ivory Tower Journal (2026).

Swygert, John. “Simulation Framework: Multiscale Field Gradient Modeling.” Ivory Tower Journal (2026).

Digital engagement and network theory literature.

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