Everything is declared. Every threshold is explicit. The pipeline is replicable. If you run the same analysis and get different results — that's a contribution, not a problem.
Zorthex measures one thing: how long a phenomenon exists before the public discovers it. This gap — between operational reality and public attention — is the diffusion lag (L), measured in months.
The moment a phenomenon becomes operationally real — not when it was theorised, not when it was first mentioned, but when it became infrastructure that could be used. For stablecoins: October 2014 (Tether launched, not when the idea was proposed). For SMR: May 2005 (IAEA published the framework, not when nuclear energy started). Every t_start is documented with a primary source and a confidence level.
The first month Google Trends reaches ≥25/100 for the phenomenon's query, worldwide. This is not peak attention — it's the threshold where search interest becomes structurally measurable. t_peak is always after t_start. The gap between them is L.
12 consecutive months ≥ 25/100 on Google Trends = STRUCTURAL. The term has entered the public vocabulary. The threshold is conventional — like BBB in credit ratings or p<0.05 in statistics. It is not "correct." It is stable, declared, and replicable. Anyone can download the same CSV and verify.
Every case is positioned using three independent attention signals. They measure different things at different levels of rigour. The strength is not that all three are quantitative — it's that they are independent.
Zorthex measures intentional attention — a person decides to search or read. Social media measures passive exposure — an algorithm decides what you see. The first is demand. The second is supply.
The difference is between a thermometer and a heater. Social media is a heater. Zorthex reads the thermometer.
Classification answers one question: did this phenomenon enter the public vocabulary? Once earned, permanent — like a credit rating at issuance. The data that produced the classification is locked and archived.
The term entered the public vocabulary. Permanent classification. The 12-month rule is the rule. No exceptions, no margin adjustment.
The window is open. Consolidation not yet confirmed. May promote to STRUCTURAL or retreat to Spike & Retreat.
Breakout occurred, consolidation did not. Attention rose above 25 but fell back before completing 12 consecutive months. The phenomenon was noticed but not absorbed. New category, August 2026 revision. 10 cases identified.
Short, intense attention burst that collapsed. Never sustained. The term was noise, not signal. Examples: Metaverse (9 max), NFT (8 max), ICO Boom (3 max).
The phenomenon's peak predates Google Trends data (pre-2004) or the attention signal is confounded by an external shock (e.g. COVID). Retained for completeness, excluded from L averaging.
The same STRUCTURAL classification can mean very different things. Streaming (218 months above threshold) and SMR (12 months exact, then retreated) both carry STRUCTURAL. The classification is correct for both. The operational reality is different.
Operational status is the second axis — a weather reading on top of the climate classification. It is applied only to STRUCTURAL cases and updated at every 90-day revision. It does not modify the classification. It describes the current state of attention.
Permanent vocabulary. Will not return below threshold. Examples: Streaming (218mo), iPhone (208mo), TikTok (78mo), ESG (69mo).
Consolidated and alive. The term is current. Examples: Stablecoins (~31), RWA Tokenization (40), mRNA Cancer Vaccines (100).
The term consolidated once — attention is now fading. May return (cyclical) or continue declining (terminal). Examples: SMR (23, 12mo exact then retreated), Basel III (24, mature term in decline).
Tied to market cycles or recurring events. Not a decline — a rhythm. Examples: Bitcoin (21, follows market cycles), Cryptocurrency (20).
Near-zero score, no prospect of return. The classification is permanent — the word is not. Example: Facebook (5, replaced by "Meta").
The three sources do not always agree. When they disagree, the disagreement is informative.
Google Trends high, Wikipedia low, both stable. The phenomenon is operational but not informational — people search to act but don't study to understand. Typical of B2B phenomena: Endpoint Security, Open Banking. The specialist uses it; the public doesn't read about it.
Wikipedia declining while Google Trends holds or rises. Discovered in 5 of 7 cases verified at three sources in the August 2026 revision:
Stablecoins: Wiki −46% YoY. Post-Quantum Cryptography: −12%. Zero Day: −42%. Endpoint Security: −35%. Open Banking: −34%.
The pattern means: the specialist audience is leaving Wikipedia because they already know the subject. The public is arriving on Google because they're discovering it. The informational layer contracts while the operational layer holds. This is the signature of a technology transitioning from specialist knowledge to public vocabulary — the exact moment the diffusion lag is closing.
Not all breakouts are alike. The Zorthex dataset identifies four distinct mechanisms by which public attention reaches a phenomenon.
Attention breaks out on a regulatory event. Slow multi-year ramp, then a single document or law triggers the breakout. Examples: Stablecoins (GENIUS Act), PQC (NIST publication), GDPR. Most common regime in the dataset.
Attention is driven by market activity, media coverage, or consumer adoption. No single trigger — a convergence of signals. Examples: Bitcoin, GLP-1/Ozempic, TikTok, Buy Now Pay Later.
Breakout driven by accumulated institutional weight — multiple large players entering simultaneously. No single regulatory event, no consumer wave — pure institutional momentum. Examples: RWA Tokenization (BlackRock + Franklin Templeton + Securitize), Cloud Computing.
A single explosive event generates massive but unsustained attention. The spike is sharp, the collapse is rapid. Typical of bubbles and crises. Examples: Metaverse (Meta rebrand), NFT (Beeple sale), GDPR (implementation deadline).
Every t_start and every claim in a ZCR report is documented with a source and a confidence level. The levels are:
| Level | Definition | Example |
|---|---|---|
| A | Government document, regulatory filing, peer-reviewed publication, international institutional publication | GENIUS Act (Congress.gov), IAEA TECDOC-1451, NRC Standard Design Approval |
| B | Corporate announcement, verified industry report, major journalism (with byline) | BlackRock BUIDL launch, Token Terminal market data, Securitize NYSE filing |
| C | Industry aggregation, secondary journalism, analyst estimate, community data | Bank of America research note (via Motley Fool), Reddit community assessment |
| Primary | Data collected directly by Zorthex | Google Trends CSV download, Wikipedia pageviews via Wikimedia API |
The dataset is revised every 90 days. Each revision may promote, reclassify, or add cases. Operational status is updated. CSV snapshots are locked and archived. Previous revisions are documented.
Current revision: August 2026 (v2.2 · 71 cases)
Previous revision: May 2026 (v2.0 · 70 cases)
Next revision: November 2026
Classifications are permanent — they are never downgraded. A STRUCTURAL case remains STRUCTURAL even if the current score drops to zero. The operational status captures what happens after classification. This mirrors credit ratings: the rating at issuance is a historical fact; the current outlook is a separate assessment.
It does not predict. Zorthex measures when public attention arrives. It does not predict whether it will arrive, or what happens to the market after it does. The framework is a timing instrument, not a forecasting model.
The threshold is conventional. 25/100 is a choice, not a law. A phenomenon of extreme sectoral importance that never reaches 25 is real but invisible to this framework. The threshold measures public legibility, not relevance.
Google Trends normalisation changes. The same query downloaded at different times produces different absolute values (the peak is always normalised to 100 relative to the query window). This is why CSVs are locked and timestamped. Directional signals are stable; absolute values may differ across downloads.
Reddit is qualitative. Declared. The community signal is directional (assente / nascente / matura), not quantified. This is a design choice — see "Why Not Social Media" above.
Wikipedia has no single page for some phenomena. Topics fragmented across multiple pages (e.g. RWA Tokenization) produce qualitative rather than quantitative Wikipedia signals. Declared per case.
Retrospective, not real-time. STRUCTURAL classification requires 12 months of data — it is always at least 12 months behind the moment of breakout. The operational status (updated every 90 days) partially addresses this. The conditional monitoring framework in ZCR reports provides forward-looking trigger conditions.