From c69b037f1ba7f5eb5be1c7de000f8d6cddef08c2 Mon Sep 17 00:00:00 2001 From: tegwick Date: Tue, 13 Oct 2026 13:28:07 +0200 Subject: [PATCH] ASSEMBLY-WP-0010-T08: 22 verified full-checkpoint source specs; catalog access classes corrected Co-Authored-By: Claude Opus 5.5 Assistant: claude-code Assistant-Model: opus Assistant-Process: 14913@bnt-lap001 Assistant-Session: add7ef69-b901-4756-9a67-34cf1125d96f --- checkpoints/IndicatorCatalog.md | 22 ++++--- checkpoints/sources.json | 62 +++++++++---------- .../ASSEMBLY-WP-0010-reality-checkpoints.md | 8 ++- 3 files changed, 52 insertions(+), 40 deletions(-) diff --git a/checkpoints/IndicatorCatalog.md b/checkpoints/IndicatorCatalog.md index c2eced7..b324ea2 100644 --- a/checkpoints/IndicatorCatalog.md +++ b/checkpoints/IndicatorCatalog.md @@ -49,9 +49,17 @@ where they apply. Column *Access*: **api** = machine-readable source that can get a `sources.json` entry; **file** = downloadable tables without a stable API; -**manual** = report, paywall or registration, collected by hand. The access -values are not yet verified against web-fetcher. Verification and wiring -belong to ASSEMBLY-WP-0010-T08. +**manual** = report, paywall or registration, collected by hand. + +**Fetched automatically (`sources.json`, `kind: full`, verified 2026-10-13):** +H1 (US, DE, AR), H3 (US, DE, AR), H4 (EU, DE), K1 (US, EU, DE; AR as GDP per +person employed), F1 (US; EU, DE via unit labour cost as a proxy), F-R1 (US), +F-R2 (EU, DE), G4 (US quits rate), X2 (AR, open-access share = two counts), +E1 (US, DE, AR), E2 (EU, DE), E4 (EU, DE). Everything else is collected by +hand at reviews, even where a source is marked `api`. Probes failed for N1 +(no FRED series; use the Census BDS tables) and Q1 (the AI Incident Database +has no simple REST endpoint). X1 is reachable, but the API gives package +counts, not reuse, so it is not wired. Cadence: **a** annual, **q** quarterly, **m** monthly, **irr** irregular or multi-year survey waves. Bundles are collected at full (annual) checkpoints @@ -115,7 +123,7 @@ Gap: no source separates founders without inherited wealth. | G1 | O | Intergenerational income mobility | US | Opportunity Insights | file | irr | | G2 | O | Re-employment of displaced workers by age | US | BLS Displaced Worker Survey (= F6) | file | biennial | | G3 | St | Data and service portability rules in force | EU, US, AR | EU Data Act, DMA; legislative trackers (with L7) | manual | irr | -| G4 | B | Job-to-job transition rate | US, EU | Census J2J; Eurostat LFS `lfsi_long_e01` | api | q | +| G4 | B | Voluntary job switching: quits rate (US); job-to-job transitions (EU) | US, EU | BLS JOLTS via FRED; Eurostat LFS | api (US) / file (EU) | m / a | | H5 | Su | Perceived freedom of choice (shared with H) | US, DE, AR | WVS; Gallup | file | irr | Gap: restartability (time × cost × restrictions) has no direct source; it @@ -125,7 +133,7 @@ needs a composite built from F-R4, G2 and G4. | ID | Kind | Indicator | Jurisdiction | Source | Access | Cadence | |---|---|---|---|---|---|---| -| N1 | O | Firm entry and exit rates (business dynamism) | US | Census Business Dynamics Statistics | api | a | +| N1 | O | Firm entry and exit rates (business dynamism) | US | Census Business Dynamics Statistics | file | a | | N2 | O | Diversity of model publishers (entropy of downloads by organization) | global | Hugging Face Hub API | api | q | | F2 | St | Concentration per component class | US, EU | competition authorities, trackers | manual | a | | N3 | B | Multi-vendor use of AI models and clouds by firms | US, EU | vendor and analyst surveys | manual | irr | @@ -137,7 +145,7 @@ Gap: interoperability test pass rates have no public series. | ID | Kind | Indicator | Jurisdiction | Source | Access | Cadence | |---|---|---|---|---|---|---| -| Q1 | O | Reported AI incidents and time to response | global | AI Incident Database | api | irr | +| Q1 | O | Reported AI incidents and time to response | global | AI Incident Database | file | irr | | Q2 | St | Press freedom; freedom of academic and cultural expression | US, EU, DE, AR | RSF World Press Freedom Index; V-Dem | file | a | | Q3 | St | Concentration of news and information channels | US, DE, AR | Reuters Institute Digital News Report; StatCounter (search share) | file | a / m | | Q4 | B | Share of AI-mediated decisions with retrievable provenance | EU | AI Act transparency obligations (with L7) | manual | irr | @@ -164,7 +172,7 @@ needs Q1 or Q2 alongside. Disagreement between them is the finding. | ID | Kind | Indicator | Jurisdiction | Source | Access | Cadence | |---|---|---|---|---|---|---| | E1 | O | Territorial CO₂ emissions | US, EU, DE, AR | Global Carbon Budget; Our World in Data | api | a | -| E2 | O | Domestic material consumption per capita | US, EU, DE, AR | UNEP IRP Global Material Flows; Eurostat `env_ac_mfa` | file / api | a | +| E2 | O | Domestic material consumption per capita | US, EU, DE, AR | Eurostat `env_ac_mfa` (EU, DE); UNEP IRP Global Material Flows (US, AR) | api / file | a | | E3 | St | Electricity mix (low-carbon share) | US, EU, DE, AR | Ember | api | m | | E4 | St | Circular material use rate | EU, DE | Eurostat `env_ac_cur` | api | a | | F8 | B | Data-center energy use | US, EU | IEA; national energy agencies | manual | a | diff --git a/checkpoints/sources.json b/checkpoints/sources.json index c4bbd01..7c02483 100644 --- a/checkpoints/sources.json +++ b/checkpoints/sources.json @@ -1,37 +1,35 @@ { "note": "Machine-readable sources for checkpoint indicators (checkpoints/IndicatorCatalog.md). Fetched with web-fetcher. 'manual' = no machine-readable source yet; collected at reviews.", "specs": [ - {"id": "L1-US", "indicator": "L1", "kind": "light", "jurisdiction": "US", - "label": "Firms using AI in any business function (last two weeks)", - "adapter": "xlsx_wide", "url": "https://www.census.gov/hfp/btos/downloads/National.xlsx", - "sheet": "Response Estimates", "filter": {"Question ID": "7", "Answer ID": "1"}, - "unit": "% of employer firms", "source": "U.S. Census Bureau, Business Trends and Outlook Survey (BTOS)"}, - {"id": "L1-US-next6m", "indicator": "L1", "kind": "light", "jurisdiction": "US", - "label": "Firms expecting to use AI in the next six months", - "adapter": "xlsx_wide", "url": "https://www.census.gov/hfp/btos/downloads/National.xlsx", - "sheet": "Response Estimates", "filter": {"Question ID": "24", "Answer ID": "1"}, - "unit": "% of employer firms", "source": "U.S. Census Bureau, Business Trends and Outlook Survey (BTOS)"}, - {"id": "L2-EU-DE", "indicator": "L2", "kind": "light", "jurisdiction": "EU, DE", - "label": "Enterprises (10+ employees) using at least one AI technology", - "adapter": "eurostat", "dataset": "isoc_eb_ai", - "filters": {"geo": ["EU27_2020", "DE"], "unit": "PC_ENT", "size_emp": "GE10", "indic_is": "E_AI_TANY"}, - "split_by": ["geo"], "last": 1}, - {"id": "L3-AR", "indicator": "L3", "kind": "light", "jurisdiction": "AR", - "label": "AI use by firms or workers", "adapter": "manual", - "source": "INDEC / national ICT surveys; OECD / IDB regional data"}, - {"id": "L4", "indicator": "L4", "kind": "light", "jurisdiction": "global", - "label": "AI task usage by occupation; automation vs. augmentation", "adapter": "manual", - "source": "Anthropic Economic Index (dataset releases)"}, - {"id": "L5-US", "indicator": "L5", "kind": "light", "jurisdiction": "US", - "label": "Private fixed investment in information processing equipment (SAAR)", - "adapter": "http_csv", "url": "https://fred.stlouisfed.org/graph/fredgraph.csv?id=Y033RC1Q027SBEA", - "value_column": "Y033RC1Q027SBEA", "period_column": "observation_date", - "unit": "billions of USD, SAAR", "source": "BEA via FRED (Y033RC1Q027SBEA)"}, - {"id": "L6", "indicator": "L6", "kind": "light", "jurisdiction": "US, EU, AR", - "label": "Public sentiment toward AI", "adapter": "manual", - "source": "Pew Research; Eurobarometer; Edelman Trust Barometer; Latinobarómetro"}, - {"id": "L7", "indicator": "L7", "kind": "light", "jurisdiction": "US, EU, AR", - "label": "AI legislation milestones since last checkpoint", "adapter": "manual", - "source": "official journals; legislative trackers"} + {"id": "L1-US", "indicator": "L1", "kind": "light", "jurisdiction": "US", "label": "Firms using AI in any business function (last two weeks)", "adapter": "xlsx_wide", "url": "https://www.census.gov/hfp/btos/downloads/National.xlsx", "sheet": "Response Estimates", "filter": {"Question ID": "7", "Answer ID": "1"}, "unit": "% of employer firms", "source": "U.S. Census Bureau, Business Trends and Outlook Survey (BTOS)"}, + {"id": "L1-US-next6m", "indicator": "L1", "kind": "light", "jurisdiction": "US", "label": "Firms expecting to use AI in the next six months", "adapter": "xlsx_wide", "url": "https://www.census.gov/hfp/btos/downloads/National.xlsx", "sheet": "Response Estimates", "filter": {"Question ID": "24", "Answer ID": "1"}, "unit": "% of employer firms", "source": "U.S. Census Bureau, Business Trends and Outlook Survey (BTOS)"}, + {"id": "L2-EU-DE", "indicator": "L2", "kind": "light", "jurisdiction": "EU, DE", "label": "Enterprises (10+ employees) using at least one AI technology", "adapter": "eurostat", "dataset": "isoc_eb_ai", "filters": {"geo": ["EU27_2020", "DE"], "unit": "PC_ENT", "size_emp": "GE10", "indic_is": "E_AI_TANY"}, "split_by": ["geo"], "last": 1}, + {"id": "L3-AR", "indicator": "L3", "kind": "light", "jurisdiction": "AR", "label": "AI use by firms or workers", "adapter": "manual", "source": "INDEC / national ICT surveys; OECD / IDB regional data"}, + {"id": "L4", "indicator": "L4", "kind": "light", "jurisdiction": "global", "label": "AI task usage by occupation; automation vs. augmentation", "adapter": "manual", "source": "Anthropic Economic Index (dataset releases)"}, + {"id": "L5-US", "indicator": "L5", "kind": "light", "jurisdiction": "US", "label": "Private fixed investment in information processing equipment (SAAR)", "adapter": "http_csv", "url": "https://fred.stlouisfed.org/graph/fredgraph.csv?id=Y033RC1Q027SBEA", "value_column": "Y033RC1Q027SBEA", "period_column": "observation_date", "unit": "billions of USD, SAAR", "source": "BEA via FRED (Y033RC1Q027SBEA)"}, + {"id": "L6", "indicator": "L6", "kind": "light", "jurisdiction": "US, EU, AR", "label": "Public sentiment toward AI", "adapter": "manual", "source": "Pew Research; Eurobarometer; Edelman Trust Barometer; Latinobarómetro"}, + {"id": "L7", "indicator": "L7", "kind": "light", "jurisdiction": "US, EU, AR", "label": "AI legislation milestones since last checkpoint", "adapter": "manual", "source": "official journals; legislative trackers"}, + {"id": "H1-US", "indicator": "H1", "kind": "full", "jurisdiction": "US", "label": "Healthy life expectancy at birth (both sexes)", "adapter": "http_json", "url": "https://ghoapi.azureedge.net/api/WHOSIS_000002?$filter=SpatialDim%20eq%20'USA'%20and%20Dim1%20eq%20'SEX_BTSX'&$orderby=TimeDim%20desc&$top=1", "path": "value.0.NumericValue", "period_path": "value.0.TimeDim", "unit": "years", "source": "WHO Global Health Observatory (WHOSIS_000002)"}, + {"id": "H1-DE", "indicator": "H1", "kind": "full", "jurisdiction": "DE", "label": "Healthy life expectancy at birth (both sexes)", "adapter": "http_json", "url": "https://ghoapi.azureedge.net/api/WHOSIS_000002?$filter=SpatialDim%20eq%20'DEU'%20and%20Dim1%20eq%20'SEX_BTSX'&$orderby=TimeDim%20desc&$top=1", "path": "value.0.NumericValue", "period_path": "value.0.TimeDim", "unit": "years", "source": "WHO Global Health Observatory (WHOSIS_000002)"}, + {"id": "H1-AR", "indicator": "H1", "kind": "full", "jurisdiction": "AR", "label": "Healthy life expectancy at birth (both sexes)", "adapter": "http_json", "url": "https://ghoapi.azureedge.net/api/WHOSIS_000002?$filter=SpatialDim%20eq%20'ARG'%20and%20Dim1%20eq%20'SEX_BTSX'&$orderby=TimeDim%20desc&$top=1", "path": "value.0.NumericValue", "period_path": "value.0.TimeDim", "unit": "years", "source": "WHO Global Health Observatory (WHOSIS_000002)"}, + {"id": "H3-US", "indicator": "H3", "kind": "full", "jurisdiction": "US", "label": "Public expenditure on education", "adapter": "http_json", "url": "https://api.worldbank.org/v2/country/USA/indicator/SE.XPD.TOTL.GD.ZS?format=json&mrnev=1", "path": "1.0.value", "period_path": "1.0.date", "unit": "% of GDP", "source": "World Bank WDI (SE.XPD.TOTL.GD.ZS)"}, + {"id": "H3-DE", "indicator": "H3", "kind": "full", "jurisdiction": "DE", "label": "Public expenditure on education", "adapter": "http_json", "url": "https://api.worldbank.org/v2/country/DEU/indicator/SE.XPD.TOTL.GD.ZS?format=json&mrnev=1", "path": "1.0.value", "period_path": "1.0.date", "unit": "% of GDP", "source": "World Bank WDI (SE.XPD.TOTL.GD.ZS)"}, + {"id": "H3-AR", "indicator": "H3", "kind": "full", "jurisdiction": "AR", "label": "Public expenditure on education", "adapter": "http_json", "url": "https://api.worldbank.org/v2/country/ARG/indicator/SE.XPD.TOTL.GD.ZS?format=json&mrnev=1", "path": "1.0.value", "period_path": "1.0.date", "unit": "% of GDP", "source": "World Bank WDI (SE.XPD.TOTL.GD.ZS)"}, + {"id": "H4-EU-DE", "indicator": "H4", "kind": "full", "jurisdiction": "EU, DE", "label": "Adult participation in learning, last 4 weeks (25-64)", "adapter": "eurostat", "dataset": "trng_lfse_01", "filters": {"geo": ["EU27_2020", "DE"], "sex": "T", "age": "Y25-64", "unit": "PC"}, "split_by": ["geo"], "last": 1}, + {"id": "K1-US", "indicator": "K1", "kind": "full", "jurisdiction": "US", "label": "Nonfarm business labour productivity (output per hour)", "adapter": "http_csv", "url": "https://fred.stlouisfed.org/graph/fredgraph.csv?id=OPHNFB", "value_column": "OPHNFB", "period_column": "observation_date", "unit": "index 2017=100", "source": "BLS via FRED (OPHNFB)"}, + {"id": "K1-EU-DE", "indicator": "K1", "kind": "full", "jurisdiction": "EU, DE", "label": "Real labour productivity per hour worked, change on previous year", "adapter": "eurostat", "dataset": "nama_10_lp_ulc", "filters": {"geo": ["EU27_2020", "DE"], "na_item": "RLPR_HW", "unit": "PCH_PRE"}, "split_by": ["geo"], "last": 1}, + {"id": "K1-AR", "indicator": "K1", "kind": "full", "jurisdiction": "AR", "label": "GDP per person employed (constant 2021 PPP $)", "adapter": "http_json", "url": "https://api.worldbank.org/v2/country/ARG/indicator/SL.GDP.PCAP.EM.KD?format=json&mrnev=1", "path": "1.0.value", "period_path": "1.0.date", "unit": "constant 2021 PPP $", "source": "World Bank WDI (SL.GDP.PCAP.EM.KD)"}, + {"id": "F1-US", "indicator": "F1", "kind": "full", "jurisdiction": "US", "label": "Nonfarm business labour share", "adapter": "http_csv", "url": "https://fred.stlouisfed.org/graph/fredgraph.csv?id=PRS85006173", "value_column": "PRS85006173", "period_column": "observation_date", "unit": "index 2017=100", "source": "BLS via FRED (PRS85006173)"}, + {"id": "F1-EU-DE", "indicator": "F1", "kind": "full", "jurisdiction": "EU, DE", "label": "Nominal unit labour cost per hour, change on previous year (labour-share proxy)", "adapter": "eurostat", "dataset": "nama_10_lp_ulc", "filters": {"geo": ["EU27_2020", "DE"], "na_item": "NULC_HW", "unit": "PCH_PRE"}, "split_by": ["geo"], "last": 1}, + {"id": "F-R1-US", "indicator": "F-R1", "kind": "full", "jurisdiction": "US", "label": "High-propensity business applications (monthly, SA)", "adapter": "http_csv", "url": "https://fred.stlouisfed.org/graph/fredgraph.csv?id=BAHBATOTALSAUS", "value_column": "BAHBATOTALSAUS", "period_column": "observation_date", "unit": "applications, SA", "source": "Census Business Formation Statistics via FRED (BAHBATOTALSAUS)"}, + {"id": "F-R2-EU-DE", "indicator": "F-R2", "kind": "full", "jurisdiction": "EU, DE", "label": "Business registrations and bankruptcy declarations (SCA)", "adapter": "eurostat", "dataset": "sts_rb_q", "filters": {"geo": ["EU27_2020", "DE"], "indic_bt": ["REG", "BKRT"], "nace_r2": "B-S_X_O_S94", "s_adj": "SCA", "unit": "I21"}, "split_by": ["geo", "indic_bt"], "last": 2}, + {"id": "G4-US", "indicator": "G4", "kind": "full", "jurisdiction": "US", "label": "Quits rate, total nonfarm (voluntary job switching)", "adapter": "http_csv", "url": "https://fred.stlouisfed.org/graph/fredgraph.csv?id=JTSQUR", "value_column": "JTSQUR", "period_column": "observation_date", "unit": "% of employment", "source": "BLS JOLTS via FRED (JTSQUR)"}, + {"id": "X2-AR-oa", "indicator": "X2", "kind": "full", "jurisdiction": "AR", "label": "Open-access works with an Argentine institution, publication year 2025", "adapter": "http_json", "url": "https://api.openalex.org/works?filter=institutions.country_code:AR,publication_year:2025,is_oa:true&per-page=1", "path": "meta.count", "unit": "works", "source": "OpenAlex", "note": "Publication year is fixed in the URL; move it to the last complete year at each annual review. Share = oa / all."}, + {"id": "X2-AR-all", "indicator": "X2", "kind": "full", "jurisdiction": "AR", "label": "All works with an Argentine institution, publication year 2025", "adapter": "http_json", "url": "https://api.openalex.org/works?filter=institutions.country_code:AR,publication_year:2025&per-page=1", "path": "meta.count", "unit": "works", "source": "OpenAlex", "note": "Publication year is fixed in the URL; move it to the last complete year at each annual review. Share = oa / all."}, + {"id": "E1-US", "indicator": "E1", "kind": "full", "jurisdiction": "US", "label": "CO2 emissions excluding LULUCF", "adapter": "http_json", "url": "https://api.worldbank.org/v2/country/USA/indicator/EN.GHG.CO2.MT.CE.AR5?format=json&mrnev=1", "path": "1.0.value", "period_path": "1.0.date", "unit": "Mt CO2e", "source": "World Bank WDI (EN.GHG.CO2.MT.CE.AR5)"}, + {"id": "E1-DE", "indicator": "E1", "kind": "full", "jurisdiction": "DE", "label": "CO2 emissions excluding LULUCF", "adapter": "http_json", "url": "https://api.worldbank.org/v2/country/DEU/indicator/EN.GHG.CO2.MT.CE.AR5?format=json&mrnev=1", "path": "1.0.value", "period_path": "1.0.date", "unit": "Mt CO2e", "source": "World Bank WDI (EN.GHG.CO2.MT.CE.AR5)"}, + {"id": "E1-AR", "indicator": "E1", "kind": "full", "jurisdiction": "AR", "label": "CO2 emissions excluding LULUCF", "adapter": "http_json", "url": "https://api.worldbank.org/v2/country/ARG/indicator/EN.GHG.CO2.MT.CE.AR5?format=json&mrnev=1", "path": "1.0.value", "period_path": "1.0.date", "unit": "Mt CO2e", "source": "World Bank WDI (EN.GHG.CO2.MT.CE.AR5)"}, + {"id": "E2-EU-DE", "indicator": "E2", "kind": "full", "jurisdiction": "EU, DE", "label": "Domestic material consumption per capita", "adapter": "eurostat", "dataset": "env_ac_mfa", "filters": {"geo": ["EU27_2020", "DE"], "material": "TOTAL", "indic_env": "DMC", "unit": "T_HAB"}, "split_by": ["geo"], "last": 1}, + {"id": "E4-EU-DE", "indicator": "E4", "kind": "full", "jurisdiction": "EU, DE", "label": "Circular material use rate", "adapter": "eurostat", "dataset": "env_ac_cur", "filters": {"geo": ["EU27_2020", "DE"], "unit": "PC"}, "split_by": ["geo"], "last": 1} ] } diff --git a/workplans/ASSEMBLY-WP-0010-reality-checkpoints.md b/workplans/ASSEMBLY-WP-0010-reality-checkpoints.md index 22eb040..153d0df 100644 --- a/workplans/ASSEMBLY-WP-0010-reality-checkpoints.md +++ b/workplans/ASSEMBLY-WP-0010-reality-checkpoints.md @@ -134,7 +134,7 @@ register the repo with the State Hub (`statehub register`). ```task id: ASSEMBLY-WP-0010-T08 -status: todo +status: done priority: medium state_hub_task_id: "85cb9eb0-911e-57ec-9cf0-ef886b3589bb" ``` @@ -144,3 +144,9 @@ web-fetcher can retrieve it and add a `sources.json` spec (`kind: full`) with an adapter. Mark any that fail as `manual` in the catalog. Do this before the first annual full checkpoint. Full runs pick up every spec, so test each adapter with `--no-llm` before committing. + +**Done 2026-10-13:** 22 `kind: full` specs, all probed live with web-fetcher. +A full fetch returns 36 observations: 31 ok, and 5 `not_found`, which are the +manual light specs. The catalog lists the automated IDs and corrects the +access class of N1, Q1, G4 and E2. The X2 publication year is fixed in the URL +and must be moved forward at each annual review.