iso3-to-iso2 mapped SJM to NO, so every research fetch keyed through it filed
Norway's figures under Svalbard. With SJM -> SJ on main, the four fetched tables now
hold what the pipeline produces for Svalbard itself, re-fetched for SJ through each
script's own code:
network-operators 60 Norwegian networks -> none (RIPEstat: 0 ASNs for SJ)
platform-reachability 15 rows of Norway's OONI -> 15 not-measured (no SJ probes)
social-platforms 8 rows of Norway, 2026-07 -> 4 rows of Svalbard, 2026-09
messaging-channels 3 rows from Norway's store -> none (no SJ storefront)
Norway is not added: it is not in the DX country spine these tables are built over,
so no build produces Norway rows here. Svalbard's own people group rows elsewhere
are untouched, and every other country's rows are unchanged and in order.
The classifier matched "line" as a bare word, so "LINE MAN: Food Delivery & more"
(Thailand) and "Malé Taxi Line" (Maldives) each ranked as the country's top messaging
app, ahead of WhatsApp. LINE now matches only as the whole name or before a colon.
The classifier moves to app-kind.mjs so the fetcher and the table builder share it,
and the builder re-derives each kind from the app name, so a cached fetch made under
an older rule cannot carry its answer forward. The headline count is computed from
the rows instead of typed: WhatsApp (Messenger or Business) is top in 73 countries.
