Datasets:

AFOS-Analytics1
/

usa-2024-electoral-divergence

	# Data Dictionary — AFOS USA 2024 Electoral Divergence

	> 🌐 EN — Data dictionary for the AFOS USA 2024 electoral-divergence dataset (polls × prediction markets × press). · PT — Dicionário de dados do dataset AFOS EUA 2024 (pesquisas × mercados de previsão × imprensa). · ES — Diccionario de datos del dataset AFOS EE. UU. 2024 (encuestas × mercados de predicción × prensa).
	> Column names and definitions below are kept in English (CSV/academic standard). · Os nomes e definições de coluna seguem em inglês (padrão dos CSVs). · Los nombres y definiciones se mantienen en inglés.

	Poll figures trace to a named pollster's published release, compiled from FiveThirtyEight's public 2024 president-poll database (Wayback snapshot of 4 Nov 2024). Market odds come from public Polymarket markets; press anchors are public articles archived in the Wayback Machine. Nothing is imputed or smoothed; missing values are left blank.

	## `polls/usa-national-polls.csv` (long format)

	One row per candidate per poll. National general-election polls only (state = blank, stage = general, cycle = 2024), Harris & Trump. 3,691 rows.

	\| Column \| Type \| Notes \|
	\|--------\|------\|-------\|
	\| `poll_id` \| integer \| FiveThirtyEight poll identifier. \|
	\| `pollster` \| string \| Polling firm (e.g. "Siena/NYT", "Marquette Law School", "Emerson"). \|
	\| `end_date` \| date \| End of fieldwork (`YYYY-MM-DD`). \|
	\| `sample_size` \| integer \| Sample size. \|
	\| `population` \| string \| Sampled population: `lv` (likely voters), `rv` (registered voters), `a` (all adults). \|
	\| `numeric_grade` \| number \| FiveThirtyEight pollster quality rating (0–3 scale), where published. \|
	\| `methodology` \| string \| Survey mode (e.g. "Online Panel", "Live Phone", "IVR/Online"). \|
	\| `candidate` \| string \| `Harris` or `Trump`. \|
	\| `pct` \| number \| National voting intention for that candidate, %. \|
	\| `source_url` \| string \| Primary source URL for the poll, as recorded by FiveThirtyEight. \|

	## `data/usa-winner-market-timeseries.csv` — PRIMARY AXIS (electoral college)

	Daily Polymarket win-probability from the "Presidential Election Winner 2024" market (who becomes president; total volume ≈ US$ 3.7 bn — the largest election market in history). 613 rows.

	\| Column \| Type \| Notes \|
	\|--------\|------\|-------\|
	\| `date` \| date \| `YYYY-MM-DD` (daily fidelity). \|
	\| `candidate` \| string \| `Trump` or `Harris`. \|
	\| `win_prob_pct` \| number \| Implied probability of winning the presidency, % (0–100). \|

	## `data/usa-popularvote-market-timeseries.csv` — COUNTERPOINT (popular vote)

	Daily Polymarket win-probability from the "Popular Vote Winner 2024" market (who wins more popular votes; total volume ≈ US$ 628 M). This is the market that favored Harris and erred. 615 rows. Same columns as the winner series; `win_prob_pct` = implied probability of winning the popular vote.

	## `data/usa-poll-aggregate-timeseries.csv`

	Trailing 7-day simple average of national poll vote share. 1,322 rows.

	\| Column \| Type \| Notes \|
	\|--------\|------\|-------\|
	\| `date` \| date \| `YYYY-MM-DD`. \|
	\| `candidate` \| string \| `Trump` or `Harris`. \|
	\| `poll_vote_share_pct_7dma` \| number \| 7-day trailing average of national vote-share polls ending on or before `date`, %. \|

	## `data/usa-divergence-timeseries.csv`

	Market × poll divergence for the Harris–Trump head-to-head period (after Biden withdrew, 21 Jul 2024). 218 rows. Axis = the winner (electoral-college) market.

	\| Column \| Type \| Notes \|
	\|--------\|------\|-------\|
	\| `date` \| date \| `YYYY-MM-DD` (market date). \|
	\| `candidate` \| string \| `Trump` or `Harris`. \|
	\| `market_win_prob_pct` \| number \| Winner-market implied probability of winning the presidency, %. \|
	\| `poll_vote_share_pct` \| number \| National poll vote share (7-day trailing average), %. \|
	\| `naive_gap_pp` \| number \| `market_win_prob_pct − poll_vote_share_pct`, in percentage points. Different units (probability vs share): reported raw, not scale-reconciled. The sign and trajectory are the signal, not the magnitude. \|

	## `press/usa-2024-press-timeline.csv`

	Curated, Wayback-archived press anchors cross-referencing the market×poll divergence. 7 rows.

	\| Column \| Type \| Notes \|
	\|--------\|------\|-------\|
	\| `date` \| date \| Article publication date (`YYYY-MM-DD`). \|
	\| `outlet` \| string \| Publishing outlet (Fortune, Al Jazeera, NBC News, NPR, PBS NewsHour, CNN). \|
	\| `headline` \| string \| Exact article headline. \|
	\| `theme` \| string \| `market_poll_divergence` · `poll_near_tie` · `market_conviction_whale` · `result_validator` · `market_vindicated` · `poll_accuracy_postmortem`. \|
	\| `axis` \| string \| Which AFOS axis the anchor maps to (`market`, `poll`, `market×poll`, `result`). \|
	\| `tier` \| string \| `anchor` (core) or `secondary`. \|
	\| `primary_source` \| string \| Underlying primary source where the carrier differs (e.g. "Associated Press (race call)", "Wall Street Journal (original)"). \|
	\| `url` \| string \| Live article URL (verified HTTP 200). \|
	\| `wayback_url` \| string \| Internet Archive Wayback Machine snapshot (permanent citation). \|

	## `data/usa-case-summary.json`

	Machine-readable summary of the validated case: result (electoral college + popular vote), both markets with eve-of-vote probabilities and verdict, final poll average, and the honesty note (two markets disagreed; the validator is the real result).

	## Raw provenance

	`data/usa-winner-poly-raw.json` and `data/usa-popularvote-poly-raw.json` — raw Polymarket payloads (event metadata + per-candidate daily price histories) for both markets, kept verbatim for provenance.

	## `data/usa-structural-context.csv`

	Structural country context from the World Bank, complementary to the divergence data: it frames the country, it does not predict the electoral outcome. Long/tidy format, one row per indicator, latest available year per indicator.

	\| Column \| Type \| Notes \|
	\|--------\|------\|-------\|
	\| `category` \| string \| `governance`, `economy`, or `education`. \|
	\| `indicator` \| string \| Machine code (e.g. `political_stability`, `gdp_usd`, `expected_years_schooling`). \|
	\| `label` \| string \| Human-readable indicator name (English). \|
	\| `value` \| number \| Governance on a 0-100 scale; economy in US$ / %; education in % of GDP or years. \|
	\| `unit` \| string \| `index_0_100`, `USD`, `percent`, or `years`. \|
	\| `year` \| integer \| Reference year of the value (latest available). \|
	\| `source` \| string \| World Bank WGI (governance, via Data360) or WDI (economy & education). \|
	\| `iso3` \| string \| ISO 3166 alpha-3 country code. \|

	Both sources are open-licensed (CC BY 4.0) and keyless. Governance = Worldwide Governance Indicators; economy & education = World Development Indicators.

	# Data Dictionary — AFOS USA 2024 Electoral Divergence

	> 🌐 EN — Data dictionary for the AFOS USA 2024 electoral-divergence dataset (polls × prediction markets × press). · PT — Dicionário de dados do dataset AFOS EUA 2024 (pesquisas × mercados de previsão × imprensa). · ES — Diccionario de datos del dataset AFOS EE. UU. 2024 (encuestas × mercados de predicción × prensa).
	> Column names and definitions below are kept in English (CSV/academic standard). · Os nomes e definições de coluna seguem em inglês (padrão dos CSVs). · Los nombres y definiciones se mantienen en inglés.

	Poll figures trace to a named pollster's published release, compiled from FiveThirtyEight's public 2024 president-poll database (Wayback snapshot of 4 Nov 2024). Market odds come from public Polymarket markets; press anchors are public articles archived in the Wayback Machine. Nothing is imputed or smoothed; missing values are left blank.

	## `polls/usa-national-polls.csv` (long format)

	One row per candidate per poll. National general-election polls only (state = blank, stage = general, cycle = 2024), Harris & Trump. 3,691 rows.

	\| Column \| Type \| Notes \|
	\|--------\|------\|-------\|
	\| `poll_id` \| integer \| FiveThirtyEight poll identifier. \|
	\| `pollster` \| string \| Polling firm (e.g. "Siena/NYT", "Marquette Law School", "Emerson"). \|
	\| `end_date` \| date \| End of fieldwork (`YYYY-MM-DD`). \|
	\| `sample_size` \| integer \| Sample size. \|
	\| `population` \| string \| Sampled population: `lv` (likely voters), `rv` (registered voters), `a` (all adults). \|
	\| `numeric_grade` \| number \| FiveThirtyEight pollster quality rating (0–3 scale), where published. \|
	\| `methodology` \| string \| Survey mode (e.g. "Online Panel", "Live Phone", "IVR/Online"). \|
	\| `candidate` \| string \| `Harris` or `Trump`. \|
	\| `pct` \| number \| National voting intention for that candidate, %. \|
	\| `source_url` \| string \| Primary source URL for the poll, as recorded by FiveThirtyEight. \|

	## `data/usa-winner-market-timeseries.csv` — PRIMARY AXIS (electoral college)

	Daily Polymarket win-probability from the "Presidential Election Winner 2024" market (who becomes president; total volume ≈ US$ 3.7 bn — the largest election market in history). 613 rows.

	\| Column \| Type \| Notes \|
	\|--------\|------\|-------\|
	\| `date` \| date \| `YYYY-MM-DD` (daily fidelity). \|
	\| `candidate` \| string \| `Trump` or `Harris`. \|
	\| `win_prob_pct` \| number \| Implied probability of winning the presidency, % (0–100). \|

	## `data/usa-popularvote-market-timeseries.csv` — COUNTERPOINT (popular vote)

	Daily Polymarket win-probability from the "Popular Vote Winner 2024" market (who wins more popular votes; total volume ≈ US$ 628 M). This is the market that favored Harris and erred. 615 rows. Same columns as the winner series; `win_prob_pct` = implied probability of winning the popular vote.

	## `data/usa-poll-aggregate-timeseries.csv`

	Trailing 7-day simple average of national poll vote share. 1,322 rows.

	\| Column \| Type \| Notes \|
	\|--------\|------\|-------\|
	\| `date` \| date \| `YYYY-MM-DD`. \|
	\| `candidate` \| string \| `Trump` or `Harris`. \|
	\| `poll_vote_share_pct_7dma` \| number \| 7-day trailing average of national vote-share polls ending on or before `date`, %. \|

	## `data/usa-divergence-timeseries.csv`

	Market × poll divergence for the Harris–Trump head-to-head period (after Biden withdrew, 21 Jul 2024). 218 rows. Axis = the winner (electoral-college) market.

	\| Column \| Type \| Notes \|
	\|--------\|------\|-------\|
	\| `date` \| date \| `YYYY-MM-DD` (market date). \|
	\| `candidate` \| string \| `Trump` or `Harris`. \|
	\| `market_win_prob_pct` \| number \| Winner-market implied probability of winning the presidency, %. \|
	\| `poll_vote_share_pct` \| number \| National poll vote share (7-day trailing average), %. \|
	\| `naive_gap_pp` \| number \| `market_win_prob_pct − poll_vote_share_pct`, in percentage points. Different units (probability vs share): reported raw, not scale-reconciled. The sign and trajectory are the signal, not the magnitude. \|

	## `press/usa-2024-press-timeline.csv`

	Curated, Wayback-archived press anchors cross-referencing the market×poll divergence. 7 rows.

	\| Column \| Type \| Notes \|
	\|--------\|------\|-------\|
	\| `date` \| date \| Article publication date (`YYYY-MM-DD`). \|
	\| `outlet` \| string \| Publishing outlet (Fortune, Al Jazeera, NBC News, NPR, PBS NewsHour, CNN). \|
	\| `headline` \| string \| Exact article headline. \|
	\| `theme` \| string \| `market_poll_divergence` · `poll_near_tie` · `market_conviction_whale` · `result_validator` · `market_vindicated` · `poll_accuracy_postmortem`. \|
	\| `axis` \| string \| Which AFOS axis the anchor maps to (`market`, `poll`, `market×poll`, `result`). \|
	\| `tier` \| string \| `anchor` (core) or `secondary`. \|
	\| `primary_source` \| string \| Underlying primary source where the carrier differs (e.g. "Associated Press (race call)", "Wall Street Journal (original)"). \|
	\| `url` \| string \| Live article URL (verified HTTP 200). \|
	\| `wayback_url` \| string \| Internet Archive Wayback Machine snapshot (permanent citation). \|

	## `data/usa-case-summary.json`

	Machine-readable summary of the validated case: result (electoral college + popular vote), both markets with eve-of-vote probabilities and verdict, final poll average, and the honesty note (two markets disagreed; the validator is the real result).

	## Raw provenance

	`data/usa-winner-poly-raw.json` and `data/usa-popularvote-poly-raw.json` — raw Polymarket payloads (event metadata + per-candidate daily price histories) for both markets, kept verbatim for provenance.

	## `data/usa-structural-context.csv`

	Structural country context from the World Bank, complementary to the divergence data: it frames the country, it does not predict the electoral outcome. Long/tidy format, one row per indicator, latest available year per indicator.

	\| Column \| Type \| Notes \|
	\|--------\|------\|-------\|
	\| `category` \| string \| `governance`, `economy`, or `education`. \|
	\| `indicator` \| string \| Machine code (e.g. `political_stability`, `gdp_usd`, `expected_years_schooling`). \|
	\| `label` \| string \| Human-readable indicator name (English). \|
	\| `value` \| number \| Governance on a 0-100 scale; economy in US$ / %; education in % of GDP or years. \|
	\| `unit` \| string \| `index_0_100`, `USD`, `percent`, or `years`. \|
	\| `year` \| integer \| Reference year of the value (latest available). \|
	\| `source` \| string \| World Bank WGI (governance, via Data360) or WDI (economy & education). \|
	\| `iso3` \| string \| ISO 3166 alpha-3 country code. \|

	Both sources are open-licensed (CC BY 4.0) and keyless. Governance = Worldwide Governance Indicators; economy & education = World Development Indicators.