The inverse crosswalk in five more languages

Ran the inverse crosswalk over the non-English catalogs: 5,956 datasets from 11 cities in Italian, Portuguese, Spanish, German and Croatian, pooled into one run under the multilingual model. Results · python3 probe/inverse.py --language non-en

Pooled rather than run per language, because cosine similarities from two different models are not comparable and two-to-four cities per language could not support an analysis whose unit of evidence is how many cities. Pooled, they share one space and eleven cities.

Elections, in both language groups, independently

The tightest cluster in anything this project has produced: 241 datasets across 6 cities, coherence 0.90 — Milan's Elezioni Politiche: Risultati di Sezione, Madrid's Elecciones Autonómicas, polling-station-level results going back to 1996.

And the English run found the same thing separately: election results by voting station, 10 cities, coherence 0.71 — Calgary, Edmonton, and others.

Two disjoint sets of cities, two different embedding models, one category. The SDG framework has no indicator for electoral administration at all; the nearest thing the matcher can find for the English cluster is "number of local governments". Turnout, results, polling-station coverage — cities publish all of it and the framework has no word for any of it.

COVID-19 case reporting corroborates the same way: 6 English cities and 7 non-English cities, independently. The nearest indicators the matcher offers are "new HIV infections per 1,000 uninfected population" and "number of total conflict-related deaths". There is no city-level pandemic-surveillance indicator to match.

What only the non-English catalogs show

The property-valuation series is the interesting one. Housing affordability is squarely an SDG 11 concern (11.1.1 is inadequate housing), but the framework counts people in inadequate housing and the Italian cities publish what property costs, by neighbourhood, twice a year, for twenty years. Those are not the same measurement and only one of them has an indicator.

The controls came out better than the English run

11 of 11 passed, against 8 of 9 in English. These are hand-built: no city outside NYC has a graded worksheet, so the controls are datasets whose titles plainly correspond to an SDG concept, chosen across all five languages and recorded in probe/inverse_controls.json.

The two weakest are both Portuguese — Fortaleza's Número de Óbitos por Acidentes de Trânsito at the 44th percentile and Recife's Acidentes de Trânsito com Vítimas at the 32nd. Those are about as unambiguous as a match gets (deaths from traffic accidents ↔ SDG 3.6.1) and they sit in the bottom half. Portuguese retrieval is the weakest link, so the Brazilian portion of the tail is the least trustworthy part of this run, and anything resting on Fortaleza, Recife, Belo Horizonte or São Paulo alone should be read with that in mind.

A quarter of the framework was unmatched text

Both runs improved when I stopped matching against indicators that have no name.

170 of the 689 base indicators carry no name in any source we hold — they return neither metadata nor observations. The first version fell back to the DCID mnemonic, so a quarter of "the framework" was represented by query strings like DI ILL OUT. Nothing can match that, which inflates the tail — datasets look further from the framework than they are — and it put mnemonics in the nearest-indicator column, where VC VAW SXVLN was being reported as a cluster's closest SDG concept.

Excluding them moved every weak control up: Fortaleza from the 28th percentile to the 44th, Recife from the 27th to the 32nd. The framework is now represented by its 519 named indicators, and the count is stated on the page rather than folded into a total.

Two smaller fixes in the same pass. Cluster examples were being spread one-per-city to show reach, which made them unrepresentative of clusters dominated by one city — a cluster labelled for Italian property valuations was illustrated with a Madrid shop register. Each cluster now shows its most central members as well as the city spread. And the phrase view returns nothing here, which the page now says is expected by construction: Spanish, Italian, Portuguese, German and Croatian titles share essentially no bigrams.

Where it stands

The non-English run has better controls and worse coverage — 183 indicators are nearest to no dataset at all, against 69 in English, which is what you would expect from half the datasets and a weaker cross-lingual model. It is not evidence that those cities publish less.