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ASSEMBLY-WP-0010-T08: 22 verified full-checkpoint source specs; catalog access classes corrected

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Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

Assistant: claude-code
Assistant-Model: opus
Assistant-Process: 14913@bnt-lap001
Assistant-Session: add7ef69-b901-4756-9a67-34cf1125d96f
This commit is contained in:
tegwick 2026-10-13 13:28:07 +02:00
parent 620346fe84
commit c69b037f1b
3 changed files with 52 additions and 40 deletions
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22
checkpoints/IndicatorCatalog.md
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@ -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 |

62
checkpoints/sources.json
View file

@ -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}
]
}

8
workplans/ASSEMBLY-WP-0010-reality-checkpoints.md
View file

@ -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.