AI score the weight of three quick ratings and updates it with each rater's latest, evidence-weighted human rating.

How human suggestions reach team review

r sufficiently strong, discussed public feedback—places the paper in a private SQLite-to-Coda queu

Public-safe

Targeted intake and prioritization requests - 74 records across 4 paper search families and 1 crux search family

Source abstract

Quick-rate mode: for each paper, how strongly should The Unjournal commission an evaluation? (-- Strong No … ++ Strong Yes). Each click is saved to The Unjournal's server. Clicking another option for the same paper updates this browser's rating rather than adding another. Add your name or email once below; the detailed form will reuse it.

More filters and source controls DisciplineThe academic field or sub-discipline of the paper, as classified by the AI model. Used to filter by research methodology and domain expertise. All Fields ▾ AI & Data ScienceAnimal WelfareDevelopment & AgriculturalEconomicsEnvironmental & ClimateLabor, Education & HealthOtherPhilosophy & EthicsPolicy & GovernancePolitical Science & LawPsychology & BehavioralStats, Finance & Methods SourceHow the paper entered this dashboard. Source is a discovery route, not a publication venue, endorsement, or quality judgment.Unjournal database: imported from The Unjournal's existing evaluation and prioritization workflow.Targeted public-paper follow-up: an independently public paper or DOI found during a requested topic-focused search. It does not mean the author supplied or endorsed the score.Academic feeds: NBER, CEPR, and RePEc working-paper feeds; arXiv and SSRN preprints; OpenAlex and Semantic Scholar indexes.EA Forum: research linked from EA Forum posts.Research organizations: Anthropic, DeepMind, and selected AI governance or safety organizations.Legal sources: legal-scholarship searches covering OpenAlex Law, law reviews, and the Institute for Law & AI. All Sources ▾ AI Governance (arXiv)AI Safety OrgsAnthropic ResearchDeepMind ResearchEA ForumNBEROpenAlexRePEcSSRNSemantic ScholarTargeted OpenAlex intakeTargeted public-paper follow-upUnjournal databasearXiv Targeted intakeRequested, topic-focused additions outside the broad recurring scan. Use this to include all records, exclude deliberately oversampled batches, or inspect only one targeted run. Include allExclude targeted intakeOnly targeted intakeOnly: AI, global health, and development curation (3)Only: Animal-welfare focused intake (19)Only: Empirical conflict replication-game intake (14)Only: Large-N GCR and existential-risk quantitative evidence (6)Only: Transformative AI, global health, and wellbeing (16)Soil-invertebrate crux sweep (6 cruxes) Crux/PQFilter to papers that map to one or more Unjournal Pivotal Questions or community cruxes (from the cruxes explorer). "PQ match only" restricts to papers mapped to a Pivotal Question. All Has crux/PQ match PQ match only RecencyFilter papers by how recently they were released or last updated. Useful for focusing on the newest research that may benefit most from timely evaluation. Any time Last 30 days Last 3 months Last 6 months Last year Last 2 years Last 5 years ModelThe AI model used to score each paper. Papers are scored in tiers: a fast model (gpt-5.4-mini) screens all candidates, a stronger model (gpt-5.4) re-scores the top papers, and the most capable model (gpt-5.4-pro) provides detailed analysis of the highest-ranked papers. "UJ historical" papers were prioritized by the Unjournal team (not AI). All Models ▾ UJ historicalclaude-sonnet-4-6escalatedgpt-5.4gpt-5.4-minigpt-5.5gpt-5.5 (codex headless, high)gpt-5.5 (codex headless, medium)gpt-5.5 (codex headless, xhigh)manual_public_followupopus (headless) Also show small-model scored (mini/haiku) Also show UJ historical Also show legal scholarship Targeted intake and prioritization requests - 64 records across 5 paper search families and 1 crux search family This shared catalog records topic-focused searches outside the broad recurring scans. Initial and follow-up passes are combined below as one search family. A targeted label records how a record was found; it is not an endorsement, quality judgment, prevalence estimate, or completed Unjournal prioritization decision.Large-N GCR and existential-risk quantitative evidenceResearch papers (6 papers; 2026-07-30) Credible large-N, cross-country, historical, forecasting, survey, or other quantitative evidence directly bearing on global catastrophic or existential risks. Public OpenAlex search, quantitative-design triage, deduplication, and prioritization scoring. This is a separate follow-up to the empirical-conflict search. It asks whether global catastrophic and existential-risk questions have credible quantitative literatures with enough observations or repeated judgments to support evaluation or replication. It excludes primarily conceptual argument, technical engineering, and ordinary civil-conflict studies without a direct catastrophic-risk connection. Inclusion is not an endorsement or a completed Unjournal team decision. View 6 records →Transformative AI, global health, and wellbeingResearch papers (16 papers; 2026-07-24) Economic, social-science, and policy research relevant to global health and wellbeing under transformative AI, especially implications for lower-income countries. Search-pass details (2)Transformative AI, global health, and wellbeing follow-up: Second-wave public-paper search focused on first-run gaps, followed by deduplication and Codex subscription scoring. This second wave follows up the initial requested intake and deliberately targets gaps it left: public finance and tax-base resilience, distribution and social protection, AI market power, worker attitudes, and developing-economy structural transformation. It uses only independently public paper or institutional publication pages. Inclusion is not an endorsement or a completed Unjournal team decision.Transformative AI, global health, and wellbeing: Public OpenAlex search, theme-fit triage, deduplication, and Codex subscription scoring. The search follows the linked Coefficient Giving request for proposals but is an independent Unjournal intake exercise. It emphasizes non-catastrophic transformative-AI scenarios, labor markets, fiscal capacity, social protection, biomedical and health-system bottlenecks, development strategy, and aid allocation. Inclusion does not imply endorsement by Coefficient Giving or an Unjournal team decision. Coefficient Giving request for proposals. View 16 records →

Median with 80% model interval

Named public commitments

A steelman against the Bay-Area assumption that very large AI-lab-adjacent philanthropy will quickly become usable funding for AI safety and EA cause areas.

The reasoning goes that if there is always a high level of background risk to humanity, then we should expect to go extinct soon anyway, which means the importance of avoiding any one particular risk is not as valuable as it may seem. For more details see the full report here.

5 why this matters6

Newest independent response: a low-cost counterpoint

One additional written estimate is visible but not pooled

Andrew

DisciplineThe academic field or sub-discipline of the paper, as classified by the AI model. Used to filter by research methodology and domain expertise. All Fields ▾ AI & Data ScienceAnimal WelfareDevelopment & AgriculturalEconomicsEnvironmental & ClimateLabor, Education & HealthOtherPhilosophy & EthicsPolicy & GovernancePolitical Science & LawPsychology & BehavioralStats, Finance & Methods SourceHow the paper entered this dashboard. Source is a discovery route, not a publication venue, endorsement, or quality judgment.Unjournal database: imported from The Unjournal's existing evaluation and prioritization workflow.Targeted public-paper follow-up: an independently public paper or DOI found during a requested topic-focused search. It does not mean the author supplied or endorsed the score.Academic feeds: NBER, CEPR, and RePEc working-paper feeds; arXiv and SSRN preprints; OpenAlex and Semantic Scholar indexes.EA Forum: research linked from EA Forum posts.Research organizations: Anthropic, DeepMind, and selected AI governance or safety organizations.Legal sources: legal-scholarship searches covering OpenAlex Law, law reviews, and the Institute for Law & AI. All Sources ▾ AI Governance (arXiv)AI Safety OrgsANIMAL_LAWANIMAL_LAW_REVIEWAnthropic ResearchDeepMind ResearchEA ForumLaw & AI InstituteLaw reviewNBERNEP_LAWOpenAlexOpenAlex LawRePEcSSRNSSRN_LAWSemantic ScholarTargeted OpenAlex intakeTargeted public-paper follow-upUJ seedUnjournal databasearXiv Targeted intakeRequested, topic-focused additions outside the broad recurring scan. Use this to include all records, exclude deliberately oversampled batches, or inspect only one targeted run. Include allExclude targeted intakeOnly targeted intakeOnly: AI, global health, and development curation (3)Only: Animal-welfare focused intake (19)Only: Empirical conflict replication-game intake (14)Only: Large-N GCR and existential-risk quantitative evidence (6)Only: Transformative AI, global health, and wellbeing (16)Soil-invertebrate crux sweep (6 cruxes) Crux/PQFilter to papers that map to one or more Unjournal Pivotal Questions or community cruxes (from the cruxes explorer). "PQ match only" restricts to papers mapped to a Pivotal Question. All Has crux/PQ match PQ match only RecencyFilter papers by how recently they were released or last updated. Useful for focusing on the newest research that may benefit most from timely evaluation. Any time Last 30 days Last 3 months Last 6 months Last year Last 2 years Last 5 years

5. Three problems that a better estimate of the exchange rate won't fix

3. What this is based on

vignette calibration

Preliminary — do not read Unreviewed working draft, 30 July 2026. Not for circulation, citation, or quotation.

2. Bottom line

The decision this is about

A funder comparing a psychotherapy

Targeted intake provenance

Optimized messaging shifts choices towards plant-based foodsource titleExact title resolved from the linked paper's bibliographic metadata; discovery-page anchor text and model output are not used as titles.

Strong near-term candidate for animal-welfare evaluation because it tests behaviorally relevant messaging around plant-b...

2 ratings

Mar 23, 2026

Decision Relevance

abstract provenanceSource-supplied abstract wording; HTML entities and whitespace normalized. Not independently compared with the paper PDF.

Rate on a 0–100 percentile scale relative to all papers in our database (Strong Yes=top quintile, Strong No=bottom). Try to form your opinion before reading the AI discussion above. Discuss why you gave that rating.

2 ratings

Email

Rate (Team) Rate (Public) View Paper

Open the conflict shortlist

Jul 2, 2026

AI-assisted prioritization for Unjournal evaluation — Prototype, March 2026

This dashboard helps identify research of interest for Unjournal evaluation. Internal prioritization currently uses our Coda interface. (March 2026)

Early prototype (March 2026). Coverage and scoring depth will improve as we expand sources and incorporate human feedback. Scores are AI-generated suggestions to help identify candidates for evaluation.

What is The Unjournal? We commission and publish independent, public evaluations of research that can inform high-stakes global decisions. We focus on economics, quantitative social science, forecasting, and policy-relevant research—including development economics, global health, animal welfare, AI governance, climate policy, and catastrophic risks. Learn more →

A documented elicitation limitation.

displayed submittable slider defaults, so some responses may be anchored or untouched.

Direct validation of the linear WELLBY against better-specified alternatives — per Benjamin, not yet done.

would answer the question a

What would change this answer

evidence

Do if you have budget or control an instrument

Flag explicitly when a conclusion depends on the location of the neutral point.

Use directly measured life satisfaction wherever a trial reports it, in preference to a converted mental-health measure.

nd publish sensitivity bounds

1. The decision this is about

Ratios move even when coefficients do not

instruments

SDs inherit the population you measured them in A standard deviation is a property of a sample, not of a person.

Mechanically, comparisons will tend to favour interventions run in more heterogeneous populations.

2. Bottom line

The reason for the SD step is data, not theory.

income-doubling to lives-saved, lives-saved to DALYs — then backing out the remaining si

its conversion

Founders Pledge raised these questions with us through our Pivotal Questions programme;

3. What this is based on

cheap fixes are known

ny single global conversion factor therefore systematically mis-weights domains

Disability weights top out around 0.3, while depression and anxiety show losses above a full life-satisfaction point.

The likely error is directional, not symmetric. Compression and ceiling effects both push toward overstating the wellbeing gains from mental-health interventions relative to physical ones. A conversion factor that is wrong in a known direction should not be treated as noise.

han a near-1:1 point estimate implies

Do not describe it as established.

and

The SD-to-SD step is defensible as a working assumption, and it is nobody's preferred method — including its originators'. Keep using it; report it as an assumption; run sensitivity analysis. Do not describe it as established.

The question funders ask us is not "is subjective wellbeing valid." It is: can I keep using this step, and how wrong might it make my ranking?

Founders Pledge's moral weights use it. Happier Lives Institute's cost-effectiveness work generates it. Anyone reading either inherits it.

Almost every such conversion in current use

one standard deviation of improvement on a depression or anxiety instrument as one standard deviation of improvement in life satisfaction

means

Identify the fragile assumptionIn your preferred bottom-up TEA, which single assumption would break the headline result if it were wrong by one order of magnitude? Which interactions …

workpapers

traceable draft calculations

Stop and obtain specialist review

The PDF output is a draft field packet, not a filled official IRS PDF.

PDF/XLSX extraction and an AJ Bell-specific parser are not yet automated.

QEF, mark-to-market, purging elections, foreign-tax-credit allocation, and notional accumulation-unit determinations are not implemented.

The calculator refuses to guess a missing rate or cumulative interest factor.

Reset to evaluation lens Current to

Job-matching and recommendation systems affect labor-market frictions, unemployment duration, and match quality, all of which have major welfare and policy consequences.

Decision relevance

Crux/PQ match update:

API cost: not shown in this export

Human ratings visible here:

Adjust these sliders to create your own priority score, then sort by Custom weights. This changes only your browser view; it does not change The Unjournal scorer.

Human ratings visible here:

The useful question now is not whether the old paper should be revived unchanged. It is what, if anything, is worth testing under current technology and policy.

gument in an Essex Economics discussion paper and an unfinished theory project

The strongest warning comes from take-up: Ofcom reports 532,000 UK social-tariff customers in June 2025, only 8.6% of a conservative proxy for eligible households.

so "free redistribution" is too strong. The more defensible claim is narrower: this could create additional purchasing power for some low-income consumers with less public expe

lower pric

The OpportunityCard, ten years on

::: {.callout-note collapse=“true”} ## Mo

Calibration anchors are a small set of real example papers that teach The Unjournal's AI prioritization scorer what “value of evaluation” looks like in each area — including the boundary cases where a good paper is not a good candidate. Each anchor's rating is the team's real prioritization rating; the proposed lesson is an AI inference about the calibration takeaway. Use this page to confirm or correct each rating and lesson, suggest new anchors, and track the discussion.

Research training

genuinely

decent evidence AI tutors teach content well

s a possible consolidation of things I already do at a smaller scale: modeling workshops on contested quantitative questions, Fermi-estimation and parameter-elicitation sessions, and supervising early-career research

PREDOC's

Intensive research training

For a few years I've had a half-formed idea for an intensive research-training program: a few weeks of courses, a supervised project, then a conference where the work actually gets read closely. I mostly kept it in my head — I think I've floated it to one or two people. My original sketch was a fairly conventional mix of statistics, data science, economics, and behavioral science, plus supervised research. What's changed my thinking is AI: less of the mechanical work you can now hand to a machine, more judgment and understanding what results actually mean. So I'm writing it up to see whether it holds together.

Training researchers for the part AI can't do for you.

Foie gras (luxury/unstructured)

How to read each column: the conventional product's retail price; the cultivated cost the model delivers to retail (your biomass slider + scaffold for cuts + markup); their ratio R; and the estimated share of that product's own market cultivated would capture (not a share of all meat). Share-bar colour: green > 30%, amber 8–30%, red < 8%.

One caveat to this species-by-species framing (our own intuition, not in his model): especially early on, a cultivated “chicken” or “shrimp” product may be received as its own distinct food rather than competing head-to-head only with the conventional product it imitates, so cross-category substitution could matter more than a per-species contest implies.

tated expert

What drives the answer.

On agreements: we draw on the same source literature and read it similarly. Humbird’s 2021 pessimism was driven mostly by amino-acid/media cost, which is not a hard thermodynamic constraint and which Pasitka’s 2024 empirical work (hydrolysate at $0.63/L) pushed down sharply. Both of us read this as suggesting cultivated meat likely lands a few-fold above conventional meat — not at parity, but not orders of magnitude off either.

$/kg →

Cost

Share colour: green > 30%, amber 8–30%, red < 8%. Ordering across species is driven almost entirely by the price ratio R (cost fixed, conventional price varies), plus the per-tier authenticity offset — reproducing Pablo's inversion: cheap chicken and pork resist, expensive beef, seafood, and luxury foie gras are penetrable.

may capture little chicken or pork share

validates out-of-sample against plant-based milk

and its 89% mainstream split,

it fills the half we skip;

an ad hoc dependence structure

weak

h caution

price ratio R

His is the piece we have explicitly not built.

Both of us conclude

Our model

Takes cost as exogenous. Cost → retail price ratio → discrete-choice (logit) market share by species, product tier, and geography → diffusion over time.

and connect end-to-end:

The two models are complementary halves of one pipeline

Precision fermentation and plant molecular farming will reduce growth factor costs substantially

100% probability (CM_17) to this before 2036.

Before mapping disagreements, it helps to note what workshop participants broadly converged on. These are not fully settled — but they represent positions where the workshop did not surface active disagreement.

The workshop participant group

2–10× below

A "Key questions for optimists" section will be added as input from more skeptical voices is gathered.

Talk: The Unjournal (Exeter)

An interactive note

Modeling approach →

Why several of these look rough.

up and do the grant-writing.

plenty

A short snapshot of what we've built recently, and directions we could take with new funding. Made to give our team a sense of what's already underway — and an easy way to say what they'd like to work on next.

Email us your idea →

round what you'd want to do

for The Unjournal

Post or comment URL *

Cleanup mode

Cadence

Meeting purpose, agenda, or decision

Use after enough responses are in

Shared Availability

copying the participant link sends invitees to the response-only page.

oordinator return link

Meeting-room link

Offered start times ?

Meeting duration

Candidate dates ?

Request ID

Poll title

Set the meeting purpose, dates, times, and links here. Drafts save in this browser while you work. Copy or preview the participant link when the request looks right.

Claim Highlighter

Research Prioritization Dashboard

Complete Unjournal evaluations with guided metrics, 90% credible intervals, calibration practice, and multiple export formats (JSON, CSV, Markdown).

Evaluation Form

Interactive Tools

Searchable database of explicit disagreements, uncertainties, and "what would change my mind" statements from EA Forum and LessWrong posts, to inform research prioritization.

AI-assisted dashboard surfacing high-impact research candidates for Unjournal evaluation — papers discovered from multiple sources and scored for evaluation priority.

Prototype

AW cruxes / Pivotal Questions

Research database Browse candidate papers; filter to animal welfare and food systems. AW cruxes / Pivotal Question

links

What The Unjournal adds in animal welfare

Cultivated-meat costs Techno-economic cruxes, cost trajectories, forecasts, and synthesis. Join / advise / evaluate Get involved, nominate evaluators, or join the broader Unjournal network.

Published AW evaluations Public Unjournal evaluations relevant to animal welfare and adjacent topics. Plant-based substitution PQ Demand-estimation evidence, operational questions, and workshop planning. Cultivated-meat costs Techno-economic cruxes, cost trajectories, forecasts, and synthesis. Join / advise / evaluate Get involved, nominate evaluators, or join the broader Unjournal network.

See AW cruxes

evaluations

Concrete ask: Link the public outputs, suggest papers and speakers, forecast tournament questions, annotate pages with corrections, and point us to drafts or slides when authors are comfortable sharing them.

Featured work and tools

Animal-welfare economics has many high-value questions where evidence is thin, contested, or scattered across academic papers, industry reports, forecasts, and practitioner knowledge. Conventional journal review is often too slow or too detached from the decisions funders and policymakers face.

Find people and collaboration points

Send a lead Suggest papers, drafts, speakers, evaluators, or pivotal research questions.

Research candidates Find animal-welfare relevant quantitative social science and economics research. AW cruxes EA Forum cruxes filtered to animal welfare and alt-protein topics. Animal Futures Tournament Forecast concrete animal-welfare, alt-protein, policy, and AI questions. Plant-based substitution Pivotal Question on demand estimation and substitution away from animal products.

Research candidates Find animal-welfare relevant quantitative social science and economics research.

AW overview Short public framing of The Unjournal's animal-welfare evaluation work.

These are the most shareable animal-welfare economics links. The rest of the page adds context and routing.

Copy share blurb

Send research leads

Animal welfare overview

Live preview Collaborate

Unjournal tools and outputs for animal-welfare economics

What The Unjournal does for animal-welfare research

Pick a time and prepare calendar, Slack, and email drafts. 1. ReadCheck what the meeting is for and any linked context. 2. Mark timesClick individual slots, whole days, or whole time columns. 3. SubmitWait for the saved message before closing the page.

1. Describe

I'm available for Room: main Reach link ready: Phone People will get a one-click call button on your card.

I'm available for

Async voice prompts

Preview my card Current general viewer preview. Group-specific previews can be added later. Visibility preview What people see Close This preview only shows the card for someone who is allowed to see you. It does not reveal future group visibility rules. Free now