California training programs, what happened to the people who took them, and where those programs actually lead.
Not affiliated with the State of California. It uses the state's open-source design system, so a permanent notice on every page says so.
3,266 California training programs, joined to the state's own ten-year projection for the occupation each one leads to. No account, no tracking, English and Spanish.
Status: Beta. Version 0.1.0, first signed tag not yet cut. The public site, the bilingual
interface, and the data pipeline are live and covered by an automated test suite. Datasets are
tagged separately per build (latest dataset-2026-08-07). Independent personal project.
If you are a Californian deciding whether to spend months and thousands of dollars on a training program, you cannot easily find out what happened to the people who finished it. The state's workforce portal puts its training list behind an account, and the outcome data that does exist — how many people completed, how many got jobs, what they earned — is published by the federal government in a form no Californian is expected to find.
Meanwhile the state publishes excellent occupation data: what jobs are growing, in which regions, and what they pay. Nobody puts the two next to each other.
Afterward does exactly that, from public data, with no account and no tracking.
The pipeline pulls every California training program reported under WIOA — provider, cost, length, format, and the federally-reported outcome measures — and joins each one to California's own ten-year projection for the occupation it feeds: median wage, projected openings, expected entry-level education, statewide and by region.
make install # Python pipeline
make data # fetches from U.S. DOL and CA EDD, writes web/public/data/
make web-install # front end
make web-dev # http://localhost:3000The pipeline emits a full dataset (programs.json, occupations.json, coverage.json)
plus a sharded bundle the site consumes: a slim search-index.json for client-side search,
and per-program and per-occupation detail fetched only when opened.
The front end is a Next.js static export in web/ — search with filters, program detail,
occupation detail, and provider pages, in English and Spanish. It needs no server at
runtime.
make web-verify # typecheck, unit tests, contrast audit, static export, axe passmake data reaches out to the U.S. DOL and California EDD. That is fine from a laptop but
not from CI — the DOL endpoint returns 403 to GitHub Actions runners, and a build should not
fail because a third party is unreachable. A 60-program fixture is committed for that:
make data-offline # build the site dataset from fixtures/data, no network
make fixture # regenerate the fixture after a real `make data`The fixture is chosen rather than sampled, so it exercises every case the UI renders
differently: reported and unreported outcomes, a suppressed measure beside a reported one, a
shrinking occupation and a growing one, a small cohort, a competency-based program, and a
program with no matching occupation. Fixture builds are marked is_fixture: true in
coverage.json.
coverage.json is a first-class output, not a debug artifact. More than a third of
California's reported programs publish no outcome data at all — 1,209 of 3,266, while the
other 2,057 report at least one measure — and the pipeline counts and publishes that rather than hiding it. Two rules follow from this and are enforced
in code:
- A withheld or suppressed measure is
null, never0. WIOA suppresses small-cohort cells to protect participant privacy; rendering one as a zero would misrepresent a real provider's performance. - "Not reported" and "reported as zero" are different facts and must stay visually different everywhere they appear.
- And a null is not always "not reported". The scorecard writes
-1for a suppressed value everywhere except the two program-length fields, where it means the program is competency-based: it finishes when the student can do the work, so it has no fixed length by design. 12 of California's 3,266 programs say that, and the site says it back rather than calling them unreported. Every rule here cuts both ways, and this is the direction that is easier to miss: publishing an absence over a fact is the same error as publishing a zero over a blank. See PROVENANCE.md, "Notes on D1".
/outcomes-coverage/ takes that headline apart in public, in both languages: which measure is
missing, from which kind of provider, and against how large a group. It exists because nobody
else publishes the number. California's ETPL is a CalJOBS search screen with no export behind
it, so the federal scorecard is the only record the question can be asked of, and the answer
is two different gaps rather than one: 1,167 of the 1,209 silent programs filed no cohort
count either, so there is no record for a measure to be missing from. Median earnings is the
measure most often absent, and the only one whose absence cohort size does not explain.
The measures also split by who produces them, with no overlap: everything the provider files
is published more often than everything the state produces by matching a roster against wage
records. Provider categories are ordered by size and never by how much each leaves blank, with
the reporting obligations that legitimately differ between them stated beside the table: an
apprenticeship program with an empty row is doing what 20 CFR 677.230(b) asks of it, and a
community college with one is not, because California's directive exempts nobody else. Every
figure carries the program-year window and the date the record was read. The scorecard
publishes no program-year field anywhere in its data, states the window only in prose on its
About page, and its data dictionary still names an earlier year.
- No account, no tracking. Everything is public, static, and readable without logging in.
- English and Spanish from the first release, not as a later phase. A missing translation is a compile error, and a test fails if a Spanish string is left identical to the English.
- Accessible, mobile-first. The people most likely to need this are least likely to be on
a new device with a big screen.
make web-verifyruns axe over the built pages and fails on any violation, and separately computes the real WCAG contrast ratio for every foreground/background pairing the site uses, in both light and dark. - Not a government site, and it says so. The California Design System makes the pages look official. A non-affiliation notice sits in the banner landmark on every page, in both languages, rather than in footer small print.
- Reproducible.
make datarebuilds every artifact from public sources; nothing is hand-edited, and every source is recorded in PROVENANCE.md.
| Source | Provides |
|---|---|
| U.S. DOL Eligible Training Provider scorecard (WIOA ETA-9171) | Programs, providers, cost, length, CIP + SOC codes, outcome measures |
| CA EDD Long-Term Occupational Employment Projections (2024–2034) | Wages, job openings, growth, entry-level education, by region |
| CA EDD OEWS | Wage detail |
Full source list, licensing, access dates, and this project's provenance constraints are in PROVENANCE.md.
make ctdl-export writes a demonstration export of California ETPL-derived program data as
CTDL JSON-LD into dist/ctdl/: one ceterms:LearningProgram per
program the site publishes and one ceterms:CredentialOrganization per distinct provider
name, with occupation alignments (ceterms:occupationType carrying 2018 SOC codes), cost
(ceterms:estimatedCost) and reported outcome statistics as one qdata:DataSetProfile per
program carrying qdata:Metric/qdata:Observation pairs, linked to the program both ways
(qdata:relevantDataSet / qdata:relevantDataSetFor). It is a
projection of the already-built dataset — the same programs.json the site serves — so it
can never disagree with the site about what the data says, and it is deliberately not part
of make data, make build, or make verify.
What it is not: nothing here is published to, drawn from, or claimed about any registry.
The CTIDs are derived locally — ce- plus a UUIDv5 over a fixed namespace and the source's
stable program identifier, so re-export is idempotent — and are not Registry-assigned;
real CTIDs exist only where a registry assigns them. Known limit, on the record: credreg's
CTID grammar says "a standard UUID v4 prefixed with ce-", and v4 means random — the one
thing a deterministic re-export cannot be. This export chooses v5 so identity survives
re-export, and says so rather than pretending the tension away. The @id URIs live under
this project's own host for the same reason.
The dataset's honesty rules transfer whole. A suppressed or unreported measure is absent
from the CTDL entity, never zero. No property is emitted on inference: no cost when a
suppressed component makes the total a floor, no organization address (the location on a
record is the program's), no occupation title on an aggregation match (it names a broader
group than the filed code), and a program page link only where the site itself publishes
one. Every emitted term is checked against a vendored copy of the CTDL context
(src/afterward/ctdl/ctdl-context.json, retrieval provenance beside it) and the export
refuses to write a term the schema does not define. A coverage statement
(ctdl-coverage.json) is counted from the emitted graph at export time — including what
the source reports that the export deliberately does not carry, with reasons.
Outcome statistics originally used ceterms:aggregateData, which surfaced a schema gap —
ceterms:LearningProgram missing from that property's per-class enumeration — filed as
Schema-Development #1080.
The maintainers' answer settled the design: the Credential Registry no longer accepts
aggregateData for publishing, and the supported pattern is the QData layer this export
now uses. The move also made the source's completion and employment rates projectable
(qdata:percentage, source fraction × 100 — a documented unit conversion the round-trip
guard applies identically), where AggregateDataProfile had no rate property at all. Every
QData term was verified against the schema encoding fetched 2026-08-07 from credreg.net;
qdata:metricType concepts come from the machine-readable qdata:MetricCategory scheme in
that same file. qdata:DataSetTimeFrame is deliberately not emitted: the source states no
reporting-period dates, and the export does not invent them.
make install
make verify # provenance-check, lint, typecheck, test, security, auditBuilt AI-assisted (Claude Code). The honesty rules above bind the tooling as much as the author: every figure the site publishes is produced by the pipeline from the sources recorded in PROVENANCE.md, the null-versus-zero rule is enforced by tests rather than by intention, and nothing ships that the data does not support. There is no AI in the product itself: no model runs at build time or runtime, and nothing on the site is generated, summarized, or ranked by one. Decisions and their reasons are recorded as they were made, in docs/design-log.md and docs/adr/.
Per the portfolio standards set. N/A rows carry their reason and an ADR; nothing is skipped silently.
| Standard | State |
|---|---|
| Responsible-Tech Framework | Applies (honest record: docs/RESPONSIBLE-TECH-AUDITS.md) |
| Code Quality | Applies (ruff, mypy --strict, pytest with an 85% coverage floor at 92% measured, uv.lock, pre-commit) |
| Security & Supply-Chain | Applies (SHA-pinned actions, gitleaks, bandit, pip-audit, Dependabot, OIDC-only deploy with no static keys) |
| CI/CD | Applies (make verify and make web-verify run identically in CI; deploys are dispatch-only and refuse to run without CI green on the exact commit) |
| Observability | Applies (static-site tier: deploy-time guards, live-site smoke tests, quarterly upstream freshness checks; declared in docs/ROADMAP.md) |
| Accessibility | Applies (WCAG 2.2 AAA target; axe and contrast gates block the build; what automation proves and what it cannot: docs/wcag-2.2-aaa-conformance.md) |
| Internationalization | Applies (EN/ES ship together; typed modules instead of catalogs: docs/I18N.md, ADR 0002) |
| AI Evaluation | N/A (no model, prompt, retrieval, or generation surface anywhere in the product; declared in docs/ROADMAP.md) |
| Documentation | Applies (README, CHANGELOG, CONTRIBUTING, SECURITY, DISCLAIMER, PROVENANCE, design log, ADRs) |
| Quality & Metrics | Applies (metrics ledger: docs/ROADMAP.md) |
| Release & Versioning | N/A (not consumed downstream: ADR docs/adr/0001-release-and-versioning-na.md; dataset snapshots are date-tagged releases consumed only by the deploy workflow) |
Apache 2.0. Source data is U.S. Government work (public domain) and California open data; see PROVENANCE.md for per-source terms.
This is independent, unpaid work. If it has been useful to you, you can
