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

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 →

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

Free

Discussion responses (open text)

Mean: 13.5 $/kg · Geometric mean: $6.8/kg · n=8

Interactive model: public test after selection into evaluation

record

Evaluator anonymity in current Unjournal public data. A bit over half of deduplicated evaluator-paper rating pairs use anonymous or generic public identifiers: 65/113, or 57.5%. This matters because later gatekeepers see the public report, but often not a named evaluator.

That the premium persists once public evaluation is common.

Author Benefits, With Limits

Non-exclusivity: the public record can stack on a journal path.

A calibrated causal effect of public evaluation on citations or journal acceptance.

The Proposition 2 signal, made interpretable by benchmarked ratings, journal-tier equivalents, and uncertainty intervals.

backs

third category

not formal publication by The Unjournal.

downside

visible

supports conditional timing

Pr(favorable | H) = Pr(adverse | L)

favorable

The public signal Y

A Simple Base Model

author response

The Unjournal's design

as accuracy q

Rejection bias

Concern

Yes. Peer review is noisy: the 2014 NeurIPS experiment had committee disagreement on 43 of 166 duplicated papers

where The Unjournal's structure changes the mechanism from cases where private author risk remains.

elevant bar

visa

Treat this as a set of considerations, not a decision rule.

plus author response

evaluation

remaining

trongest private case is a paper already ready for serious expert scrutiny whose true quality exceeds its default credibility.

A public commitment — and a signal. “I’m willing to have this evaluated openly.” Feedback now, a public signal now — journal path still open.

The Pivotal Questions project

runs

What does open (Unjournal) evaluation provide? Now: faster, useful feedback + a credible public signal, and useful inputs to practitioners and funders. Soon: it starts to carry career value. Eventually: it can replace much or all of what we ask the journal stamp to do. Which of these would actually help your work?

outputs

quality

For research leaders & managers encouraging engagement signals a commitment to rigour, transparency & innovation — and opens the research-impact channel (our funder & practitioner network, incl. Pivotal Questions). Two audiences. Individuals/committees: a strong public evaluation should count as evidence of quality in its own right — hiring, promotion, REF narratives, grants, esteem. Research managers / those setting direction for a group or department: encouraging engagement is a visible demonstration of research rigour, transparency and openness to innovation — and brings more, faster, more transparent feedback and signals than standard peer review. Exeter needn’t lead, but it could position itself as open to this innovation. The research-impact channel is real: strong connections to funders and nonprofit practitioners, including via Pivotal Questions. If useful, happy to discuss a light next step. span.MJX_Assistive_MathML { position:absolute!important; clip: rect(1px, 1px, 1px, 1px); padding: 1px 0 0 0!important; border: 0!important; height: 1px!important; width: 1px!important; overflow: hidden!important; display:block!important; }

Each

pool

research

with apologies to a certain lager

certain

3.9

and importance if true

expressed

Full “model” (v. preliminary, ~Fable-generated with human feedback): unjournal-reluctance-note.netlify.app Reframed per your screening/sorting logic: the value is highest when you’re strong-but-under-credited or just below a bar — exactly where an extra credible signal can move you. And if committing to open evaluation becomes a positive signal in a sorting equilibrium, you want to be an early mover. The case for waiting is narrow: work that already clears the bar AND a genuinely sensitive moment — then the extra signal adds little. I’d be less worried about about “harmful criticism”: our evaluations are constructive and you get a public response; public scrutiny isn’t a bogeyman. For timing/embargo, people talk to us. span.MJX_Assistive_MathML { position:absolute!important; clip: rect(1px, 1px, 1px, 1px); padding: 1px 0 0 0!important; border: 0!important; height: 1px!important; width: 1px!important; overflow: hidden!important; display:block!important; }

Watch the 2-minute explainer

We’re working to be highly visible — so evaluations & ratings are seen before conventional journals/reviewers weigh in

The strongest private case is a paper already ready for serious expert scrutiny whose true quality exceeds its default credibility

good

Questions for you

What would make an evaluation count as evidence of quality?

Where would faster public evaluation be most useful in economic

From papers to decision-relevant uncertainty

LLM vs. human ratings: modest correlation — not aligned enough to substitute

Human judgment still central

evaluation

papers

Forecasts via Metaculus; partners incl. Institute for Replication, Center for Open Science

Questions

~9 management · 15+ advisory board · 40+ field specialists · 180+ evaluators (over half economists, over half doctorates). The point isn’t celebrity endorsement — it’s that we have enough disciplinary coverage, advisory oversight, evaluator depth, and process experience to be taken seriously. Usually 2–3 evaluators per package (not “everyone behind every evaluation”). Field specialists across eight areas help prioritise and recruit. Two of those field specialists are here at Exeter (next section). Full team at unjournal.org/team. span.MJX_Assistive_MathML { position:absolute!important; clip: rect(1px, 1px, 1px, 1px); padding: 1px 0 0 0!important; border: 0!important; height: 1px!important; width: 1px!important; overflow: hidden!important; display:block!important; }

markets

certified

R&R

The

problem

It’s a coordination problem

Replace Fear of Standing Out with Fear of Missing Out

e.g. Bonn’s tenure criterion: “at least one article in a top-5 general-interest journal.” The signal is the system. The honest diagnosis is a collective-action failure, not preference. Outside demand (funders who value research) can fund a better signal while academia decides how much to trust it — which is why an early, low-cost engagement from a place like Exeter matters. The Bonn example shows how hard-coded journal prestige has become. Detail slide. span.MJX_Assistive_MathML { position:absolute!important; clip: rect(1px, 1px, 1px, 1px); padding: 1px 0 0 0!important; border: 0!important; height: 1px!important; width: 1px!important; overflow: hidden!important; display:block!important; }

The full workflow

Coefficient

Research evaluation for choices that can’t wait. The honest origin story (keep on-slide light, say this aloud): some early funders and partners come from the global-priorities / EA-adjacent world. They’re not mainly asking “is this top-5 material?” — they’re asking “how should this change our beliefs, and what should we do differently?” They need quantified beliefs with uncertainty and explicit reasoning. That’s a different demand signal from the journal system. Important framing: this is NOT “academics don’t want to change.” Many academics dislike the current system — but individual researchers and departments can’t safely move first. It’s a coordination failure mistaken for a preference. Outside demand matters because it can pay for a better signal while academia decides how much to trust it. And the demand may grow: AI wealth may expand impact-focused philanthropy — Anthropic has confidentially filed a draft S-1 for a proposed IPO (not money in hand, but a plausible tailwind). span.MJX_Assistive_MathML { position:absolute!important; clip: rect(1px, 1px, 1px, 1px); padding: 1px 0 0 0!important; border: 0!important; height: 1px!important; width: 1px!important; overflow: hidden!important; display:block!important; }