Dynamic content model

This website is a knowledge system

Content is not a set of posts. It is a set of interconnected knowledge objects, and every page you have seen is a projection of them. One signal appears in its industry page, its trend, its tension, its inquiry, its report and the ecosystem map, automatically and consistently.

Entities

EntityDefinitionIn this corpus
IndustryA sector or ecosystem with persistent monitoring6
Organization / ActorA company, regulator, government body, research centre, association or actor class23
TechnologyA technology or technology family11
SignalOne dated, attributed, typed observation about one concept, backed by exactly one piece of evidence17
TrendA living object with stage, direction, momentum, maturity, evidence for and against, and a timeline8
EventA dated occurrence on an industry timeline18
EvidenceA document, indicator or dataset with source type, date and quality17
ClaimA statement with polarity, modality (observed, predicted, hypothetical), evidence and contestation5
TensionTwo poles with the signals strengthening each side6
InquiryA question with why it matters, triggers, evidence, actors, tensions, hypotheses and implications4
HypothesisA candidate answer with supporting and challenging signals9
ReportA versioned, living study with structured sections3
DatasetA collected corpus with provenance
GeographyCountry, macro-region, state, functional territory, municipality, infrastructure, basin11
CapabilityOne of the eight capabilities8
PlatformA running implementation6

Relations

SubjectRelationObject
Signalsupports / contradictsTrend
SignalinvolvesActor
SignalaffectsIndustry
Signaloccurs inGeography
SignaltriggersInquiry
Signalprovides evidence forClaim
SignalstrengthensTension pole
TrendinvolvesTechnology
TrendhasTimeline of events
InquiryweighsHypothesis
Hypothesissupported by / challenged bySignal
Claimcontested byEvidence
ReporttracksTrend, Tension, Inquiry
Capabilityimplemented byPlatform
PlatformdemonstratesCapability

The model aligns with the productive-ecosystem ontology used in POLIS (agents, roles, localities, signals with lifecycle status, PESTLE categories, horizons, impact and confidence levels) and with the MINERVA signal schema (concept, event type, evidence class, dimensions, sensor, source quality). See the graph.

How the corpus is produced

In production, the observatory is fed by three systems. AB-NODA's MINERVA emits signals with deterministic ids, trend snapshots with TrendScore and Confidence, actor networks and tension edges. The PESTEL scanner (POLIS) emits weak signals and traction topics with a seven-part score and ontology instances with provenance. SPHINX emits inquiries generated through five epistemic lenses over Context Briefs, with expert evaluations.

An export step maps each system's objects to this content ontology and writes JSON. The site build renders every page from that JSON. Nothing on an intelligence page is written by hand except the editorial synthesis of a report.

Status of what you are reading

Every signal on this site is a real, dated event or measurement. Each rests on one evidence object that lists its sources, with a URL, a date and the verbatim passage that supports the signal; datasets are cited by the exact query that produced the figure. The build refuses to publish a signal whose evidence has no such source.

Each evidence object is also checked by an independent model with live web search, which is not shown our sources; its verdict and citations are stored as a provenance record and shown next to the sources. Where we could not find public evidence, the item was replaced or removed rather than kept as an illustration. Analyses that are ours and not yet published, such as the Recife contracts network, say so.

Follow state (the objects you follow) lives in your browser only. Under a managed plan it moves to your observatory account.