Methodology

Methodology

GEO.or.id

Dissecting Algorithmic Versions of Truth

This page describes the methodological approach used by GEO.or.id in conducting research, analysis, and observation related to Generative Engine Optimization (GEO), artificial intelligence systems, and algorithmic information representation.

This methodology is intended to provide transparency, context, and epistemic boundaries, not guarantees of outcomes.

1. Methodological Purpose

The methodology of GEO.or.id is designed to:

  • Examine how generative AI systems interpret, structure, and reproduce information
  • Analyze entity recognition, contextual framing, and algorithmic behavior
  • Distinguish observation from inference and inference from claims
  • Prevent overgeneralization and deterministic assumptions

The methodology prioritizes understanding system behavior, not controlling it.

2. Research Orientation

GEO.or.id adopts a qualitative–analytical and observational research orientation, informed by:

  • Conceptual analysis
  • Comparative system observation
  • Iterative hypothesis testing
  • Cross-source contextual validation

Quantitative metrics may be referenced where relevant, but are not treated as absolute indicators of truth or performance.

3. Sources of Input

Research conducted under this methodology may draw from:

  • Publicly available documentation and standards
  • Observations of generative AI system outputs
  • Comparative analysis of AI-assisted search and answer systems
  • Secondary research, including academic and industry publications

GEO.or.id does not claim ownership of third-party data or systems.

4. Observational Framework

Observations are conducted under specific and documented conditions, including:

  • Time-bound system states
  • Contextual prompts or inputs
  • Platform-specific constraints

Observed behavior is treated as contextual evidence, not universal system rules.

5. Non-Deterministic Assumption

A core methodological assumption is that:

  • Generative AI systems are probabilistic and non-deterministic
  • Outputs may vary across time, context, and system updates
  • No single observation can establish a fixed causal relationship

Accordingly, findings are framed as interpretive insights, not predictive guarantees.

6. Entity-Centric Analysis

GEO.or.id emphasizes entity-based understanding, including:

  • Identification of conceptual, organizational, and system entities
  • Consistency of entity representation across contexts
  • Analysis of how entities are recognized and referenced by AI systems

Entity analysis is descriptive, not authoritative.

7. Temporal Awareness

All research acknowledges the temporal nature of AI systems.

This includes:

  • Model updates
  • Policy changes
  • Shifts in training data and inference behavior

Findings may lose relevance over time and should be interpreted accordingly.

8. Ethical and Safety Considerations

Methodological practices align with:

  • Ethics & AI Governance principles
  • AI Safety considerations
  • Responsible research conduct

GEO.or.id does not engage in deceptive, exploitative, or manipulative practices aimed at misleading AI systems or users.

9. Limitations of the Methodology

This methodology:

  • Does not guarantee visibility, accuracy, or outcomes
  • Does not claim comprehensive system coverage
  • Does not replace independent verification or expert judgment

Limitations are considered an integral part of responsible research.

10. Relationship to Other Pages

This methodology should be read in conjunction with:

  • GEO Framework
  • Experimental Notes Index
  • Dataset & Reference Index
  • Publications
  • Disclaimer
  • Corrections Policy

No single page represents the methodology in isolation.

11. Revisions and Updates

The methodology may be revised to reflect:

  • Advances in AI systems
  • New research insights
  • Ethical or regulatory developments

All substantive changes follow the Corrections Policy.

12. Closing Statement

The methodology of GEO.or.id is a framework for inquiry, not a mechanism of control.
Its purpose is to support rigorous, transparent, and ethically grounded exploration of how algorithmic systems construct and mediate information.

“This methodology is not intended as a guarantee of commercial performance, but rather as a framework for evaluating the behavior of AI systems.”