feat(parliament): surface sansad-semantic-crawler v1.0.0 discourse layer

[skishchampi]

Summary

Reframes the data-page parliament section from what MPs ask to how the State responds when asked. Consumes the v1.0.0 analytical pipeline (extract-answers → analyse-discourse → analyse-ministry) and joins it into the public dataset.

Pipeline

• Makefile — new targets: corpus-extract-answers, corpus-analyse-discourse, corpus-analyse-ministry, corpus-analyse, corpus-enrich. corpus-refresh now runs the full pipeline end-to-end.
• scripts/build_parliament_libraries.py (new) — joins manifest.jsonl + analysis_discourse.jsonl + ministry_summary_qa.jsonl into the public JS export. Emits four new top-level keys: discourseSummary, ministryDiscourse, discourseExcerpts, rrrlfDeflections.

Surface

• Headline stat: Of 105 classified responses to library questions, 64 (61%) were evasive.
• "Library is a state subject" cascade: every FEDERAL_DEFLECTION response on rrrlf-tagged questions, sorted by date. The same five words appear in 1998 (HRD) and 2018 (Culture × 3) — across two decades and two political dispensations. Pairs with the existing RRRLF "two decades, a nose dive" section directly above it: Centre underfunded → Centre evaded when asked why.
• Per-ministry evasion bars with classified-N denominators (HRD: 60% on N=15 of 83; Culture: 40% on N=15 of 67) — honest framing for the small classifier sample.
• Taxonomy of evasion: single-column row list, one verbatim phrase per label as the hero (italic quote), one-line citation, collapsible full passage. Labels covered: REJECTED, SUBSTITUTED, FEDERAL_DEFLECTION, DEFLECTED, DATA_WITHHELD, STRUCTURAL_REFUSAL, CONSTITUTIONAL_DEFAULT, REPRESENTATIONAL_SILENCE.
• Method note with corpus sizes + classifier name + repo link.
• Old keyQuestions + topTags rendering demoted to a collapsible <details> block.

Followup tracked separately

Issue CommonerLLP/sansad-semantic-crawler#41 — the v1.0.0 discourse classifier is voice-blind. Adding voice/passive_ratio/agent_named lands as v1.1.0 in the upstream package; this consumer is structured to receive those fields when they arrive.

Test plan

• CI passes (link checker + html validation)
• After merge, load /data/#parliament:
  • Headline stat renders with red 61% and the lede sentence
  • RRRLF cascade shows the 1998 + three 2018 deflections, dates left-rail
  • Per-ministry bars render with rate + classified/total N
  • Each evasion row renders the italic pattern + cite line; "Read the passage" expands
  • Method note lists corpus stats and links the upstream repo

[chatgpt-codex-connector]

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[chatgpt-codex-connector]

Stop pre-escaping titles in export builder

The builder escapes title before writing JSON, but the UI escapes the same field again in assets/main.js when rendering both the evasion rows and the RRRLF cascade. This double-escaping turns legitimate characters like & into visible entities (&) whenever a parliamentary title contains special characters, so end users see corrupted text instead of the original title.

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[chatgpt-codex-connector]

Make corpus-enrich require analysis outputs

corpus-enrich only depends on corpus-export, but build_parliament_libraries.py reads analysis_discourse.jsonl and ministry_summary_qa.jsonl and silently treats missing files as empty arrays. As a result, running make corpus-enrich (or running corpus-refresh with parallel make) can produce a “successful” artifact with zeroed discourse metrics and missing sections instead of failing fast, which makes the published dataset silently incomplete.

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