3.1.1. Independence, scope, and investor-first framing

This report is written from an independent investor/community perspective. It is not produced by a validator, core team, or any L2 project.

Investor-first framing means:

• The report prioritizes investor impact (risk, liquidity/access, value preservation, credibility, adoption drivers) over validator convenience or internal politics.

• Validators are assessed as a critical operational stakeholder group—but not as the “center of gravity” of what matters.

3.1.2. Primary source corpus used in this report

(Living corpus)

This report uses a mixed-method evidence base. Blockchain-derived data is treated as primary whenever available, and qualitative sources are used to explain, validate, or challenge interpretations.

Primary sources currently in scope (already provided):

• Independent research and analyses produced by Classic Chaos Podcast, including validator-focused research (the “PRO LUNC VALS” series) and compiled governance/forum discussions.

• Independent third-party reports about Terra Classic (historic and recent), used for triangulation.

• Telegram chat histories of major Terra Classic stakeholder groups (e.g., L1 Devs & Validators, LUNC Validators, Terra Classic Founders).

• Full topic/discussion history from Terra Classic forums (Classic Agora, Common.xyz, New Agora).

• A structured Problems & Opportunities compilation.

truth.terra-classic.money - On-chain dashboards/tools already provided :

• Monthly Active Wallets (participants per month based on transaction senders/recipients on Terra Classic L1).

• Community Pool dashboard (balances and flow analysis).

• Governance participation dashboard(s).

• Validator set + voting behavior + estimated income dashboard(s).

• Proposals dashboard (all proposals that reached voting stage since May 2022).

Off-chain attention and distribution sources (new additions):

• Google Trends (search interest over time for Terra Classic–related queries; used as an attention proxy and narrative timing signal).

• Google Analytics data from the terra-classic.money website (traffic sources, geography, returning vs new, engagement, retention proxies, and content demand).

Primary sources expected during report creation (to be added):

• Additional dashboards, exports, charts, and tools provided during production (including any new data sources we identify as required).

Important: living corpus

The “primary source corpus” is intentionally not closed. One of the functions of this report is to identify missing observability and force expansion of data sources when conclusions would otherwise rely on assumptions. This corpus will be updated throughout production and then finalized at publication (with a sources register in the Appendices).

3.1.3. Evidence hierarchy (what counts most and why)

To prevent “argument by loudness,” evidence is prioritized as follows:

1. On-chain measurements (transactions, fees, stake distribution, governance votes, treasury flows)

2. Derived telemetry / operational artifacts (validator performance distributions, incident logs where verifiable)

3. Governance records and repositories (proposal texts, votes, implementation evidence, code commits/releases)

4. Independent reports (triangulation and historical context, not single-source truth)

5. Chat histories and stakeholder testimony (qualitative evidence; handled with bias controls)

6. Media and social narratives (context only; never treated as proof)

3.1.4. Claim taxonomy (mandatory labels)

Every meaningful claim in the report is labeled with one of the following:

• Measured: Computed from blockchain-derived dashboards, chain data, reproducible quantitative extraction, or verifiable analytics (e.g., Trends/GA).

• Documented: Supported by governance records, repo history, published audits, signed statements, or public artifacts.

• Reported: Stakeholder statements from chat logs, interviews, or forum posts (with bias disclosure).

• Inferred: A conclusion derived from multiple signals; assumptions are stated explicitly.

• Speculative: Forward-looking hypothesis; separated visually and minimized.

Rule: Conclusions and recommendations must not be built solely on Reported evidence without triangulation.

Practical examples:

• Measured (off-chain attention): Google Trends worldwide (1 Jan 2022–25 Jan 2026) shows average interest indices: LUNC 13, luna classic 7, Terra Classic 5, USTC 3, with a major attention spike around mid-2022 and much lower baseline thereafter.

• Measured (distribution performance): Google Analytics for terra-classic.money (custom range 1 May 2025–29 Jan 2026) shows 45k active users, 45k new users, and 45s average engagement time, plus a heavy Directtraffic share (~60k sessions), followed by Organic Social (~11k), Referral (~6.1k), and Organic Search (~2.1k).

3.1.5. Triangulation rules (how we validate contested claims)

Terra Classic is a politicized environment with frequent “contested narratives.” To prevent bias:

• Any controversial finding must be supported by at least two independent evidence types, preferably with Measured/Documented as one of them.

• If sources conflict, the report will:

  1. describe what conflicts,

  2. show what is verifiable,

  3. label the remaining portion as “contested” or “uncertain.”

3.1.6. Time windows, baselines, and comparability

This report covers May 2022 → 2026, with the default quantitative lens being:

• trailing 12 months (current state), and

• “since May 2022” (structural trend).

Where possible, metrics will be presented as:

• absolute values, and

• normalized values (per day, per active wallet, per validator, per unit of fees, etc.)
  to improve comparability across time.

3.1.6.1. Using off-chain attention (Google Trends) and site analytics (GA)

Off-chain signals do not prove protocol health, but they can prove something else: attention cycles, narrative penetration, and distribution performance.

Rules:

• Google Trends is used only as an attention proxy and is never treated as proof of adoption, revenue, or network fundamentals.

• Google Analytics is used to measure information demand and distribution (how users discover Terra Classic information, what they read, where they drop off).

• Where GA/Trends diverge from on-chain reality, the report treats this as a diagnostic clue (e.g., “attention without usage,” “usage without awareness,” “marketing disconnected from product reality”).

Additional GA examples (from screenshots, same date range):

• Geography: top active-user countries include United States (~4.6k), Türkiye (~3.8k), Indonesia (~2.4k), India (~2.2k), Thailand (~2.0k), Brazil (~1.8k), China (~1.7k).

• Retention proxy: cohort table shows Week-1 retention around 3.7%, then 2.1%, 1.5%, 0.6%, 0.4% by Week-5 (interpretation: content demand exists, but repeat engagement is limited—this will be analyzed later in the Marketing/Distribution and Brand chapters).

• Content demand: one “Terra Classic Blockchain – …” page shows ~95k views, suggesting a small number of pages drive disproportionate attention (important for narrative and distribution strategy).

3.1.7. Accountability and identification rules

The report may name validators, organizations, and individuals when accountability is relevant and evidence-based, under strict rules:

• Naming is permitted when the actor’s identity is already public and connected to their role (validator operator identity, org branding, public governance authorship, public statements).

• The report will not publish private personal data or non-public identifiers.

• Critiques focus on role-based responsibility and observable actions/outcomes (votes, proposal authorship, delivery/execution, treasury decisions, uptime/performance, public comms).

• Claims implying wrongdoing require a higher bar: Documented evidence is required; otherwise the report uses system framing (incentives, process failures, competence gaps).

3.1.8. Data architecture (how data becomes analysis)

The report uses a simple pipeline model:

Sources → Extraction → Normalization → KPI computation → Exhibits → Findings → Recommendations

• Extraction: dashboards, exports, structured forum indices, curated artifacts from the provided corpus

• Normalization: time windows, deduplication, actor identity mapping (where public), consistent units

• Computation: KPI formulas (defined in Appendix B)

• Exhibits: charts and tables tied to specific claims

• Findings: evidence-labeled statements

• Recommendations: always linked to measurable KPIs

Off-chain measurement layer: Google Trends (attention cycles) and Google Analytics (distribution and information consumption), used primarily in the Marketing/Distribution and Brand/Narrative chapters.

Cross-signal triangulation: where possible, correlate attention spikes (Trends/GA) with on-chain events (upgrades, incidents, major proposals) to distinguish “narrative volatility” from “fundamental change.”