Methodology

How events are classified, how risk indices and market linkages are constructed, how procurement notices are filtered, how organisations are resolved, and how the difference between a record and an inference is drawn.

Event Classification Framework

Articles are ingested from a curated set of sources. A structured classifier assigns an event type, severity (1–5), and a confidence score (0.0–1.0). The intent is consistency and auditability rather than prediction.

Severity scoring

  • 1 — Routine developments with limited operational impact.
  • 2 — Notable developments; localized or low-intensity effects.
  • 3 — Material escalation, sustained disruption, or major policy action.
  • 4 — Significant escalation with broad operational or strategic consequences.
  • 5 — High-intensity conflict or systemic disruption with strategic impact.

Confidence scoring

Confidence reflects the system’s certainty in the structured labels, based on clarity and corroboration in the source text. Low confidence does not imply the event is unimportant; it indicates ambiguity or limited detail.

Event type taxonomy

Event types are mapped into stable buckets (e.g., kinetic, diplomacy, sanctions, procurement, cyber). Taxonomy can expand over time without changing historical records.

Risk Index Construction

Risk indices are aggregated from structured events over rolling windows. A baseline approach is:

  • Severity × confidence aggregation to reduce over-weighting uncertain classifications.
  • Regional buckets (e.g., global, NATO, BRICS, Indo-Pacific) derived from deterministic country/regional groupings.
  • Rolling windows (daily and weekly) to support consistent comparisons over time.

Market Linkage Model

Market analysis is descriptive and window-based:

  • Weekly return is computed from the first and last close inside a weekly window.
  • Correlation uses Pearson correlation between risk index series and market return series where sufficient data exists.
  • Limitations: correlations are not causal and may be unstable across time periods and regimes.

Weapons-to-Company Mapping

Weapons systems referenced in events can be linked to manufacturers and (where applicable) representative tickers. The mapping is curated and scope-limited; it does not imply procurement, revenue share, or exposure magnitude.

Procurement Relevance and Sector Classification

Tender and award notices are taken from Canadian federal open data under the Open Government Licence. Two judgements are applied, and they are kept separate:

  • Relevance decides whether a notice is a defense-sector opportunity at all. Roughly four in ten published notices pass; the rest are genuine government purchasing that has nothing to do with defense industry.
  • Sector assigns a notice to a named defense area. Only about one in five relevant notices lands in one, so an unclassified group is published rather than forcing every notice into a category it does not belong in.

A sector is anchored on a supply-classification code only where the code class is the sector. Anchoring on a general electronics or hardware class puts a rack-mounted console into radar and autonomy, which is why the rule is narrow. The classification ruleset is versioned, and stored notices are replayed against it when it changes, so a figure can always be traced to the rules that produced it.

Two limits are stated on the page itself rather than buried here. Tender notices carry no monetary field of any kind — value exists only on award notices, so any total covers awards alone. And closing dates are published without a time zone offset.

Organisation Resolution

Supplier names as government publishes them are resolved into organisations. The rule is structural, not a similarity score: two names may merge only when every token they do not share is generic. Two companies sharing a family name but differing in what they do — an oil company and a shipbuilder — cannot merge at any threshold, in any tuning, while four spellings of the same subsidiary collapse into one.

How a name was matched determines how the link is drawn. An exact, alias or normalised match is a government record and is drawn filled and solid. A partial token overlap is an inference and is drawn hollow and dashed. A published supplier name we have never seen becomes a provisional organisation rather than a discard: refusing to record it would throw away a government fact. What is withheld is the judgement, not the name.

Government Structure and Parliamentary Activity

No machine-readable organisation chart of the Government of Canada exists, so the structure is curated, with a fetched and confirmed source URL for every node. Bodies whose published location could not be verified were dropped rather than guessed at.

This is deliberately not a searchable directory of public servants. A person exists only as the holder of a public office and is reachable only by selecting that office; an office shows a name only where it has been read off a government page and dated. Most offices therefore show an empty seat, and the ratio of filled to total is published rather than hidden.

The committee that held a meeting is a record. What the meeting was about is keyword-matched, and therefore always presented as an inference. Sources that are declared but not yet connected report as skipped with a reason; a source that looks healthy while returning nothing is the worst outcome, so not-connected and broken are shown as different states.

How Provenance Is Drawn

Across every visualization, certainty is carried by fill and stroke, never by colour. Something published by a government source is filled and solid; something inferred or unverified is hollow and dashed. That distinction survives greyscale printing and colour blindness. A relationship nobody publishes is drawn broken rather than hidden, because a chain that quietly omitted the missing hop would imply the data connects when it does not.

Limitations & Disclaimers

  • Not financial advice; outputs are informational and descriptive.
  • Data coverage varies by source availability and publication practices.
  • Automated classification can be uncertain; confidence scores are provided for transparency.
This page is intended to improve transparency and interpretability. The system emphasizes reproducibility, clear assumptions, and conservative handling of uncertainty.