Every speech in the Index is split into individual sentences and used as the unit of analysis. A sentence is a tractable, recognisable unit: it has clear boundaries (punctuation), it is short enough that a single dominant frame usually applies, and it maps cleanly to the way readers and listeners actually parse rhetoric.
An alternative unit — the quasi-sentence used by parts of the political-text-analysis literature — splits sentences further whenever a single sentence carries more than one distinct argument. Quasi-sentences are useful in principle but fraught in practice: their boundaries are subjective, two coders rarely segment a long sentence the same way, and the resulting tag fragments are hard to compare across a corpus that already spans 240 years of evolving prose. The Index therefore stays at the sentence level and tags a sentence by its dominant frame.
For each dimension, every sentence is passed to a frontier language model with a fixed rubric (see below). The model returns one tag per dimension, or null when no tag fits. Classification is run in batches of 40 sentences per request to avoid drop-off, with a single retry on length mismatch. Tags are stored alongside the sentence text in each speech's JSON file.
The rubric is identical for every model call. Speakers, dates, and external metadata are deliberately withheld from the prompt — the tag must come from the sentence in front of the model, not from priors about who is speaking.
Where resources allow, a sample of speeches is also tagged by human reviewers using the same rubric. Reviewers see the sentence and the rubric definitions but not the AI's tag.
AI and human tags are then compared sentence by sentence. A sentence's dominant classification is the one both methods agree on; disagreements are flagged for re-tagging at scale and used to refine the rubric for the next pass. The Index is therefore a moving artefact — rubrics get tighter as the corpus grows.
Each sentence is tagged on six dimensions. Use the options below as the working rubric.
Time orientation reveals whether a leader is framing the moment as one of inheritance, of present condition, or of imminent change — a quick proxy for whether a speech is defending a record, asserting authority, or making a promise.
was / were / did / inherited / since / in [past year]. Example: "We inherited a crisis last winter."is / are / now / today / at this moment. Example: "Inflation is plummeting."will / shall / going to / next year / by [future year]. Example: "We will rebuild this nation."Expression separates the speech's rhetorical function: factual claims, binding commitments, or calls to act. The mix exposes how much of a leader's discourse is descriptive, performative, or mobilising.
is / has / represents / means). Example: "America is the most innovative nation on earth."we will / I pledge / this administration will / we commit to. Example: "We will deliver universal childcare within four years."let us / I ask / I urge / send / stand up. Example: "I ask Congress to pass this bill before recess."Stance captures the power posture a speaker takes toward the audience — humble petitioner, equal collaborator, or judging authority. It is how the speaker positions themselves relative to those who can act on the message.
I thank / with humility / I ask for / Mr Speaker / Your Majesty. Example: "I am honoured to stand before this distinguished body."we / together / our nation / side by side, inclusive pronouns. Example: "Together we will face this challenge."I refuse / they must / I will not allow / no one can. Example: "I will not negotiate with those who would dismantle our democracy."Agency identifies who the speech casts as the actor. Patterns here reveal whether a leader is centring their own state, building solidarity with allies, or focusing the audience on adversaries.
America / our country / this administration / the United States. Example: "America led the world out of recession."our partners / NATO / the British / Japan. Example: "Our allies in Europe have stepped up sanctions enforcement."the regime in / terrorists / Beijing / Moscow / cartels. Example: "Russia continues to escalate its aggression in Ukraine."Reference tracks the scope a leader speaks to — domestic, bilateral, or regional/global. It exposes whether a speech is internally focused or projecting outward, and whose attention it seeks.
American workers / our schools / Main Street / this nation. Example: "Our schools need every dollar we can spare."with China / between the US and Japan, named pair. Example: "We will negotiate a new trade agreement with Mexico."the world / Europe / Asia-Pacific / UN / G7 / international community. Example: "The free world must stand together against authoritarianism."Capability maps each sentence onto the nine domains of the GINC National Capability Framework. It surfaces which levers of national power — hard, soft, or economic — a leader is signalling intent to invest in, defend, or wield.
artificial intelligence / chips / frontier technology / compute. Example: "We will lead the world in AI and semiconductor manufacturing."infrastructure / grid / broadband / ports / rail. Example: "We are rebuilding America's highways, bridges, and broadband."armed forces / deterrence / troops / weapons / NATO. Example: "Our armed forces remain the most lethal fighting force in history."schools / universities / skills / healthcare / workforce. Example: "Every American child deserves a world-class education."values / narrative / media / Voice of America / soft power. Example: "We must counter disinformation with the truth."rule of law / Constitution / courts / democracy / free and fair elections. Example: "The integrity of our elections is non-negotiable."deficit / debt / interest rates / Fed / balance the budget. Example: "We will balance the budget within ten years."manufacturing / made in America / factories / productivity / small business. Example: "American manufacturing is roaring back."trade deal / tariff / exports / sanctions / FDI. Example: "We will impose tariffs on any nation that dumps cheap steel into our markets."Four numeric scores characterise the texture of the prose itself, independent of the sentence-level tags. Each is computed per sentence and the speech-level score is the mean across all sentences in the address. Year and corpus aggregates further weight each speech by its word count so longer addresses pull more weight on the trend. Empty, single-token, or content-less sentences are skipped.
How approachable is the language? Higher scores mean easier reading — broadcast-era addresses sit above 70; 19th-century formal prose can drop below 40.
words/sentences equals the sentence's token count; syllables/words is the mean syllables-per-token.metrics.readability object also keeps the median, sd, min, max, and n (sentences scored) so downstream charts can show distribution, not just mean.What reading grade does the text demand? Lower is easier — modern campaign rhetoric runs 6–8; 19th-century inaugural addresses can demand 16+.
How structurally complex is each sentence? Measured as mean dependency distance — how far, on average, a word sits from the word it modifies. Higher = nested clauses, longer reach.
en_core_web_sm dependency parse.How rare is the lexicon? Measured as average rarity of content words — common-word speeches score low; latinate, jargon-rich, or archaic speeches score high.
Transcripts are sourced from public archives. In addition to those listed below, GINC sources transcripts from public addresses to top up the index where useful coverage is missing.