Updating the number of schools

ReligiousCharacter 99 (~4k schools) and AdmissionsPolicy 9 (~5.6k) carry a
code with a blank name in the GIAS CSV; the generator skipped them so they
hit the Unknown(<code>) path — wrongly triggering the Faith-priority tag
and polluting filters. Blank-only codes now map to "" (byte-identical to
the old name pipeline); accepted_values lists extended to match the seed.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

backend/gias_codes.py

@@ -78,6 +78,7 @@ OFFICIAL_SIXTH_FORM: dict[int, str] = {
     0: "Not applicable",
     1: "Has a sixth form",
     2: "Does not have a sixth form",
+    9: "",
 }
 
 RELIGIOUS_CHARACTER: dict[int, str] = {
@@ -128,12 +129,14 @@ RELIGIOUS_CHARACTER: dict[int, str] = {
     47: "Reformed Baptist",
     48: "Roman Catholic/Anglican",
     49: "Sunni Deobandi",
+    99: "",
 }
 
 ADMISSIONS_POLICY: dict[int, str] = {
     0: "Not applicable",
     2: "Selective",
     4: "Non-selective",
+    9: "",
 }
 
 

backend/tests/test_gias_codes.py

@@ -81,3 +81,15 @@ def test_seed_matches_dictionaries():
         for row in csv.DictReader(fh):
             seed[row["field"]][int(row["code"])] = row["name"]
     assert seed == fields
+
+
+def test_blank_name_sentinel_codes_map_to_empty_string():
+    """GIAS carries codes whose (name) column is blank — e.g. ReligiousCharacter
+    99 (~4k schools) and AdmissionsPolicy 9 (~5.6k schools). The old name
+    pipeline served these as empty strings; the dictionaries must reproduce
+    that ("" is falsy, so UI tag heuristics stay silent) rather than letting
+    them hit the "Unknown (<code>)" path meant for genuinely new codes."""
+    assert RELIGIOUS_CHARACTER[99] == ""
+    assert ADMISSIONS_POLICY[9] == ""
+    assert translate(99, RELIGIOUS_CHARACTER) == ""
+    assert translate(9, ADMISSIONS_POLICY) == ""

docs/superpowers/specs/2026-07-02-ux-audit-report.md

@@ -89,7 +89,7 @@ These are strengths the fixes below must not regress:
   - Uplift: **home 46% exit rate — decrease, moderate** and **share of sessions reaching a school page — increase, moderate** (assists the majority entry path at its first interaction).
 
 - **P1.7 — The mobile hero omits the value proposition entirely** *(J1-F6)*
-  - Evidence: desktop shows the "UPDATED WITH 2026/2027 ADMISSIONS RESULTS" trust badge and the "24,000+ schools… side by side, in one place" subheading; mobile renders only the poetic H1 ("Every school in England, *compared.*") and a bare search box (`j1-home-desktop-fold.png` vs `j1-home-mobile-fold.png`).
+  - Evidence: desktop shows the "UPDATED WITH 2026/2027 ADMISSIONS RESULTS" trust badge and the "27,000+ schools… side by side, in one place" subheading; mobile renders only the poetic H1 ("Every school in England, *compared.*") and a bare search box (`j1-home-desktop-fold.png` vs `j1-home-mobile-fold.png`).
   - Criterion: mobile content parity; Nielsen #1 — first-visit orientation ("what is this, why trust it") absent on the primary viewport.
   - Argument: 63% of entries land here and 56% of traffic is mobile; a first-time visitor gets no statement of coverage, data source, or freshness above the fold. Weak value proposition at first glance is a classic bounce driver and plausibly a material slice of the 46% exit rate.
   - Recommendation: restore a compact version of the badge + one-line value prop under the mobile H1 (one text block; the fold has room above the deadline rail).

nextjs-app/components/HomeView.tsx

@@ -271,10 +271,10 @@ export function HomeView({ initialSchools, filters, totalSchools, howItWorks, ed
                 freshness, standing in for the hidden eyebrow too) on phones,
                 where every line above the fold costs. */}
             <span className={styles.heroDescriptionFull}>
-              <strong>24,000+ primary and secondary schools</strong> with Key Stage 2 SATs, GCSE results, Ofsted grades, progress scores and admissions data — side by side, in one place.
+              <strong>27,000+ primary and secondary schools</strong> with Key Stage 2 SATs, GCSE results, Ofsted grades, progress scores and admissions data — side by side, in one place.
             </span>
             <span className={styles.heroDescriptionCompact}>
-              <strong>24,000+ English schools</strong> — SATs, GCSEs, Ofsted &amp; admissions, side by side. Updated for 2026/27.
+              <strong>27,000+ English schools</strong> — SATs, GCSEs, Ofsted &amp; admissions, side by side. Updated for 2026/27.
             </span>
           </p>
         </div>

pipeline/scripts/generate_gias_codes.py

@@ -94,13 +94,23 @@ def main() -> None:
     for code_col, name_col, dict_name, field_key in FIELDS:
         pairs = (
             df[[code_col, name_col]]
-            .loc[lambda d: (d[code_col] != "") & (d[name_col] != "")]
+            .loc[lambda d: d[code_col] != ""]
             .drop_duplicates()
         )
-        mapping = sorted((int(c), n) for c, n in pairs.itertuples(index=False))
-        dupes = len(mapping) - len({c for c, _ in mapping})
-        if dupes:
-            sys.exit(f"{code_col}: {dupes} codes map to multiple names — investigate before generating")
+        by_code: dict[int, set] = {}
+        for c, n in pairs.itertuples(index=False):
+            by_code.setdefault(int(c), set()).add(n)
+        mapping = []
+        for code, names in sorted(by_code.items()):
+            named = sorted(n for n in names if n != "")
+            if len(named) > 1:
+                sys.exit(f"{code_col}: code {code} maps to multiple names {named} — investigate before generating")
+            # Codes that only ever appear with a blank (name) are GIAS
+            # "not recorded" sentinels (e.g. ReligiousCharacter 99,
+            # AdmissionsPolicy 9). Map them to "" so the API serves the same
+            # empty string the old name pipeline did — the "Unknown (<code>)"
+            # path is reserved for genuinely new codes.
+            mapping.append((code, named[0] if named else ""))
         lines = [f"{dict_name}: dict[int, str] = {{"]
         for code, name in mapping:
             escaped = name.replace('"', '\\"')

pipeline/scripts/gias_codes.py

@@ -78,6 +78,7 @@ OFFICIAL_SIXTH_FORM: dict[int, str] = {
     0: "Not applicable",
     1: "Has a sixth form",
     2: "Does not have a sixth form",
+    9: "",
 }
 
 RELIGIOUS_CHARACTER: dict[int, str] = {
@@ -128,12 +129,14 @@ RELIGIOUS_CHARACTER: dict[int, str] = {
     47: "Reformed Baptist",
     48: "Roman Catholic/Anglican",
     49: "Sunni Deobandi",
+    99: "",
 }
 
 ADMISSIONS_POLICY: dict[int, str] = {
     0: "Not applicable",
     2: "Selective",
     4: "Non-selective",
+    9: "",
 }
 
 

pipeline/transform/models/marts/_marts_schema.yml

@@ -42,12 +42,12 @@ models:
         tests:
           - accepted_values:
               severity: warn
-              values: [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]
+              values: [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 99]
       - name: admissions_policy_code
         tests:
           - accepted_values:
               severity: warn
-              values: [0, 2, 4]
+              values: [0, 2, 4, 9]
 
   - name: dim_location
     description: School location dimension with PostGIS geometry

pipeline/transform/seeds/gias_code_names.csv

@@ -53,6 +53,7 @@ phase_of_education,7,All-through
 official_sixth_form,0,Not applicable
 official_sixth_form,1,Has a sixth form
 official_sixth_form,2,Does not have a sixth form
+official_sixth_form,9,
 religious_character,0,Does not apply
 religious_character,2,Church of England
 religious_character,3,Roman Catholic
@@ -100,6 +101,8 @@ religious_character,46,Protestant/Evangelical
 religious_character,47,Reformed Baptist
 religious_character,48,Roman Catholic/Anglican
 religious_character,49,Sunni Deobandi
+religious_character,99,
 admissions_policy,0,Not applicable
 admissions_policy,2,Selective
 admissions_policy,4,Non-selective
+admissions_policy,9,