How this publication is produced
AI News Social is a weekly bilingual research publication on the social aspects of AI, AI literacy, AI tools, and higher education. Every edition is the output of a named, auditable pipeline. This page explains it at three levels of depth — pick the one you need.
Layer 1 · The short answer
Nine-criterion rubric, four categories, one Longer View, weekly.
Each week, a research pipeline searches roughly forty curated sources across news, academic repositories, and civic-tech outlets. Candidate articles are scored on nine inclusion criteria. Those that clear threshold are analyzed across eight critical-thinking dimensions and sorted into four topical categories. A library-grounded editorial layer then produces a Longer View essay, category reports, audience briefings, podcasts, and columnist pieces. Every empirical claim carries a direct citation.
Latest edition: 2026-04-26 · Last updated: April 27, 2026
Layer 2 · Rubric, categories, and grounding
What gets in, and how it's evaluated
The nine-criterion inclusion rubric
Every candidate article is scored on these nine criteria. Articles below a weighted threshold are excluded from the edition. Scores are preserved in the week's archive and exposed in the analysis page.
| Criterion | What it rewards | Weight |
|---|---|---|
| Relevance | Direct thematic fit with the edition's four categories. | 10% |
| Source authority | Institutional, academic, or domain-expert provenance. | 10% |
| Recency | Published within the edition's calendar window. | 8% |
| Reasoning quality | Clear argumentative structure; identifiable claims and warrants. | 14% |
| Evidence | Primary sources, data, or documented reporting — not opinion-only. | 14% |
| Specificity | Concrete cases, numbers, or named actors beat generalities. | 10% |
| Argumentative depth | Engages counter-arguments and edge cases, not one-sided. | 12% |
| Methodological transparency | For research: disclosed method, sample, limitations. | 12% |
| Novelty | Adds something the edition does not already cover. | 10% |
The four categories
Social Aspects of AI
Labor, inequality, governance, public reasoning, cultural effects — the human consequences of AI deployment.
AI Literacy
What people need to know to reason about AI systems — conceptual, practical, critical.
AI Tools
Concrete capabilities, product releases, technical developments — what's actually shipping and how it works.
Higher Education
The professional audience layer: teaching, research, governance, policy in colleges and universities.
The eight critical-thinking dimensions
After articles pass the rubric, each is analyzed along eight dimensions used to build the synthesis layer:
- Claim structure — what is being asserted, and how centrally.
- Evidence base — what kind of evidence supports it, how strong.
- Stance — where the piece stands on the live questions in its category.
- Framing — which narrative frame organizes the argument (progress, threat, equity, governance, etc.).
- Counter-argument engagement — how explicitly alternatives are addressed.
- Concept co-occurrence — which ideas cluster together, across the corpus.
- Causal attribution — what is claimed to cause what, with what confidence.
- Temporal orientation — retrospective, present-tense, anticipatory, or longitudinal.
Library grounding
Editorial pieces (the Longer View, category essays, audience briefings, thinker columns) are written against the current week's accepted articles plus two read-only libraries of prior thinkers' work: a general English intellectual library and a LATAM intellectual library (for the Spanish edition). This keeps the editorial voice grounded in the tradition it draws from. Library retrieval is silent — the editorial never invokes a thinker who isn't retrievable in the corpus.
Layer 3 · The full pipeline
Cell-by-cell: what runs each week
The pipeline is organized into numbered stages. Each stage is a set of Python "cells" — small, restartable modules. A full run takes roughly 8–12 hours wall-clock, bottlenecked by the evaluation and long-form generation steps. Below is the run order.
STAGE 00 Config + orchestrator bring-up
STAGE 10 Search: SerpAPI + curated RSS + academic feeds
→ raw_search_results/*.json
STAGE 20 Extraction: Diffbot + fallback scrapers
→ accepted_articles_*.csv (pre-rubric pool)
STAGE 25 Editorial instinct (V3 additions):
25_01 intellectual_lineage
25_02 knowledge_gap
25_03 topic_trajectory
25_04 topic_arc_analysis ← Longer View engine
25_05 longer_view_topic_picker
STAGE 30 Scoring: nine-criterion rubric + dimension tagging
→ accepted_articles_*.csv (post-rubric, ranked)
STAGE 40 Content synthesis:
40_01 briefing_context (per category)
40_02 podcast_generation (Opus public_content)
40_03 thinker_columns (McLuhan / Toffler / Asimov|Freire)
STAGE 50 Editorial essays (per category; Opus, library-grounded)
50_04 named-thinker deep columns
STAGE 60 Publishing:
60_01 briefings (4 HE audiences)
60_02 essays (4 categories + Longer View)
60_03–06 PDF compile (xelatex → Tufte layout)
60_07 HTML assembly (template_v3.html)
60_08 longer_view_en + longer_view_es
60_09 methodology page refresh
STAGE 80 Audio production: Chatterbox multilingual
80_01 podcast_images
80_02 podcast_composition (TTS + stitching)
80_03 website_video_prep
STAGE 90 Deploy + archive:
90_01 archive_previous (COPY semantics; landmine-safe)
90_02 deploy_website (FTP to ainews.social)
90_02_5 email_buttondown (draft to EN + ES lists)
90_03 post_deploy_verify
90_04 youtube_upload
90_05 corpus_update (sources + work planes)
90_06 weekly_intelligence_archive
Expand: model routing
All model selection goes through config/models.yaml, never hard-coded into cells.
- Public-facing content (editorials, essays, briefings, thinker columns, podcast scripts, Longer View): Anthropic Claude Opus — current routing: public_content → claude-opus-4-7
- Image prompts (editorial illustrations, category hero images): Opus 4.6 (explicit override — see feedback note)
- Image generation: Google Gemini Nano Banana Pro
- Analytical work (scoring, dimension extraction, synthesis at scale): DeepSeek cloud chat + DeepSeek-R1 reasoner
- Embeddings: OpenAI text-embedding-3-large (3,072-dim) for 2026-vintage collections; 1,536-dim retained for legacy stats
- TTS: Chatterbox multilingual (local GPU) for production; ElevenLabs for one-time voice design only
- STT / audio alignment: Faster-Whisper (local GPU)
Expand: reliability & audit
Every week's rubric scores are kept in the edition's archive and exposed on the /analysis/{week_id}/ page. Cronbach's alpha for the nine-criterion rubric is computed from paired independent re-scoring on a random sub-sample each week. Current operational target: α ≥ 0.75 across criteria. Rubric refinements are versioned in config/rubric/.
Editorial grounding is audited by the citation validator (orchestrator/citation_validator): every numerical claim or direct assertion in Longer View / essays / briefings must resolve to a bracketed reference pointing at an accepted article or a library passage. Unresolved citations are flagged at 60_07 and block deploy.
Expand: chat grounding & privacy
The per-edition chat widget is grounded only in the current week's edition — Longer View, category reports and essays, audience briefings, podcast transcripts, thinker columns, and accepted articles. It does not retrieve from prior weeks unless you explicitly ask it to compare editions.
Conversations are not used to train models. They are stored in short-lived logs for abuse prevention only, then discarded. No personal data is required to chat. The email subscription is handled by Buttondown under their privacy terms; we do not sell, share, or enrich subscriber lists.
Expand: open-source and reproducibility
The pipeline is published under CC-BY-NC for the content and MIT-style permissive licensing for reusable library code. Full source: github.com/diegobonilla/ai-news-social (V3 branch). The prompt templates that drive the editorial layer live in config/prompts_v2/ and are versioned alongside every released edition.
Questions
If something here is unclear — or if you think we're getting something wrong — write to diego@csus.edu. Corrections and criticisms are welcome and acknowledged.