The Unjournal

Making impactful research more rigorous — and rigorous research more impactful

David Reinstein · Founder & Co-Director, unjournal.org

Economics Research Away Day · University of Exeter — Friday 19 June 2026

The Unjournal — in brief

This deck was prepared for a one-time talk (University of Exeter, 19 June 2026). For the maintained, reusable version, see the generic Unjournal talk →

A grant-funded nonprofit that commissions experts to publicly evaluate and rate research, and assesses “Pivotal Questions” for stakeholders.
We aim to make impactful research more rigorous, make academic work more useful, support open science & transparency, and improve peer review — aligning research incentives with truth-seeking and social value.
Focus: economics, policy & quantitative social science with potential for global impact.
Output: unjournal.pubpub.org

Good to be back at Exeter

A typical day lecturing at Exeter uni.

Warm, peer-to-peer open — an image, not a wall of text. The story to tell here: - I was a Senior Lecturer in this department (after Essex; Berkeley PhD); ~20 years in academia before I left to pursue impact. - The Unjournal grew out of three things colliding: (1) my own (common) gripes about academic peer review; (2) wanting research to have real-world impact — the kind of thing REF impact cases at least gesture at; and (3) once I left, exposure to impact-focused funders who genuinely want to use research. - It partly started in conversations on this campus — including Miguel Fonseca’s comment: “this sounds like a lot of work.” He was right. So now I’m asking “has this work been useful enough for people here to engage with and use?” - Logistics aside: this deck is hosted and open to Hypothes.is comments — annotate it later if you like. Interrupt me; there’s a menu of ways to engage at the end, and questions I’d love your thoughts on.

1 · The limitations of journal peer-review

An old system, still running the show

The 17th-century journal became a useful filter. It shouldn’t be the only signal we coordinate on (nor the only output).

A journal-era filter still governs careers and research credibility
We already disseminate ourselves: working papers, arXiv, SSRN, RePEc, dynamic docs and web pages
So what does a journal really “sell” now? An evaluation. A stamp.

The biggest cost isn’t fees or paywalls: it’s the game

Average economics paper: 3–4 submissions before it’s placed
Reviewer time alone: a back-of-envelope ~$150M/year in econ.
The biggest cost: authors’ time — reformatting, resubmitting, journal-shopping, strategising (“spin it as a hamburgers economics paper for the American Hamburger Journal”) instead of just improving the work

★ THE PUBLISH-OR-PERISH SLOT MACHINE ★

insert: 1 finished paper · ~6 months / spin

1 · pick an arm (which journal?)

AEREJJDE

2 · pull the lever → wait ~6 months →

✗↻✓

rejectR&Raccept

PAYOUT: one line on your CV

Careers staked on a noisy, slow spin — each spin ~6 months; placement takes 2–6 years.

“Playing this game diverts us from producing the most credible, useful research.”

“Published — so stop bothering me about it”

Journals take one format: ~30 static pages (+ a 200-page appendix)
Publication says “done” → slice off the next paper
Little room for improvement, error-correction, building in place

Separating evaluation from publishing → a world of benefits

Decouple the evaluation from the 30-page PDF and it becomes a citable, first-class object — DOI, metadata, discoverable — instead of a one-shot stamp in a “PDF prison”.

Research as a living document

Evaluation isn’t chained to a 30-page PDF:

Any format — dynamic, interactive, replicable documents
Improve or extend in place → ask for further evaluation
Open evaluation feeds open science & replication

An interactive specification-curve / “multiverse” document — far easier to build now, and far more useful than static pages.

2 · What The Unjournal is

unjournal.org · info.unjournal.org · unjournal.pubpub.org

We are not a journal, we don’t “publish papers”

A non-profit commissioning open evaluation of publicly-hosted research with potential for global impact.

We commission and pay for expert evaluation; authors can also publish in a journal
Multiple evaluations + structured ratings + author response: public, with DOIs
Credible, citable peer review, not tied to a journal’s accept/reject

unjournal.org · unjournal.pubpub.org · info.unjournal.org

Funders: Survival & Flourishing Fund (largest)
Long-Term Future Fund
EA Infrastructure Fund

Why journal-independent evaluation can succeed now

Some research-users want to know more than “which journal published it”, and they want to get faster and deeper feedback.

Funders & research users who need evidence — e.g. Coefficient Giving, Survival & Flourishing Fund
They want credible expert judgment, transparent reasoning, quantified beliefs & uncertainty
Decision-relevance and value of information

↓ “but doesn’t this need everyone to move at once?”

Our 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 want quantified beliefs with uncertainty and explicit reasoning.

Some partners are skeptical and say “academics don’t want to change” – I disagree. Many academics would prefer a different system (if it was credible) — but individual researchers and departments hesitate to 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: the narrative I hear a lot is that AI wealth is likely to expand impact-focused philanthropy (Anthropic IPO, etc.)

Solving the coordination problem

Academics ~broadly agree open evaluation is better — but can’t move first alone
Funding & grantmaker incentives can tip the balance
We’re working to be highly visible — so evaluations & ratings are seen before conventional journals/reviewers weigh in
Building a bridge, not asking you to jump off.

Fear of Standing Out → Fear of Missing Out

Making it discoverable where it counts

Unjournal evaluations are indexed in Google Scholar — surfacing with the working paper, not years later. search “source:unjournal” →

Fast public evaluation/feedback: an increasing priority

One round of public evaluation → a credible output now.

A publicly citable signal after one round
Versus a traditional journal: 6+ months, R&R at best, then maybe accepted after substantial revisions
Fast-moving topics can miss the decision window

AI capabilities · AI’s impact on labour markets · policy windows.

↓ how long does it actually take?

How long does it take?

Target ~2–3 months · prioritisation → published package

Recruit ~2 evaluators — ~1–2 weeks
Evaluations (reports + ratings) — 5+ weeks (~3-week turnaround target each)
Author response — ~2 weeks (longer if revising)
Total target: ~7–10 weeks

Versus a traditional economics journal: ~1–3 years (often 24+ months to acceptance).

Self-reported evaluator effort ≈ 8–32 hours per evaluation. The target above is from our process docs; we track the dates but haven’t yet published a measured median end-to-end.

How it works

Find / receive the research
Prioritise for decision-relevance (as a team)
Recruit an evaluation manager → ~2 paid expert evaluators
Reports + ratings + author response (evaluators may adjust)
Manager synthesis → publish the package, with a DOI

Evaluators paid, named or anonymous. ▶ 2-min explainer · ↓ full workflow & video

Our workflow

Watch the 2-minute explainer

▶ Watch the 2-minute explainer on YouTube

A short narrated walk-through of the Unjournal evaluation process · youtu.be/ZCSeAmzMB50

We prioritise research for impact-potential

Prioritisation is triage, not evaluation

First question: will better evidence here change real decisions?
We do prioritize influential, widely-read work, but we don’t chase the merely clever

↓ how the triage actually runs · how the team votes

How the triage runs

Suggestor & assessor each write a short motivating discussion; the assessor rates decision-relevance & potential impact (0–100 vs the pool); the whole team votes (5-point approval scale); management finalises and liaises with authors.

How the team votes

Every candidate paper gets a team vote on impact-potential — Strong Yes / Weak Yes / Unsure / Weak No / Strong No, with vote counts and an average. This is the actual voting board (Coda).

Some considerations

What an evaluation gives you

We don’t accept/reject or assign a tier; so we benchmark instead. Two halves, equally important:

The substance

Detailed referee-style reports
The authors’ response
Editorial summary

The structured ratings

Percentile ranking
Journal-tier equivalent (0–5)
Nine criteria, with quantified uncertainty
Claim identification and assessment

All public, citable, and comparable — see the evaluator interface →

Inside the evaluation form

What an evaluator actually fills in — every rating elicited with a 90% credible interval, not a point score.

Percentile rating · Methods: justification, reasonableness, validity, robustness

0255075100

Midpoint 72 · 90% CI 66 – 78 (tight — a confident rating; one of nine criteria)

Journal-tier rating (0–5) — elicited as two separate ratings, each with its own 90% CI

“Should” — normative merit → Midpoint 3.8, 90% CI 2.7 – 4.6 (wide — more uncertain)

“Will” — predicted placement → Midpoint 3.2, 90% CI 2.5 – 3.9

012345

0 won’t publish · 1 OK · 2 marginal-B · 3 top-B · 4 marginal-A · 5 top-A (top-5: AER, QJE, Econometrica). Non-integers encouraged; gap between “should” and “will” = the placement lottery.

Claim identification & assessment — evaluators pull out the paper’s key claims and rate, for each, the strength of evidence and its implications.

Rebuilt from the live instrument · open the evaluator form →

3 · What we’ve done

Where we are now

57 evaluation packages on PubPub

100+ expert evaluations

180+ evaluators (120+ PhDs, ~40 profs)

~$450 avg evaluator payment

1,000+ structured ratings recorded

40+ field specialists

ISSN 3071-2173 · 501(c)(3) · DOIs

Founded 2022, public since 2023.

Every rating comes with a credible interval

Published evaluations only, selected for decision-relevance; sorted by rating midpoint. Dots = evaluator medians; bars = stated uncertainty (where given). ratings dashboard → · ↓ a bridge to journal tiers

Benchmarking existing signals: a known currency

Predicted vs. merited journal tier (0–5). A translation layer — not an endorsement of placement as the right endpoint.

A profile, not a single score

Three evaluated papers profiled across every criterion — percentile rank (top) and journal tier (bottom). A score on each dimension, with the strengths and weaknesses a single number would hide. explore the live dashboard →

Overall ratings by research area

Every published evaluation’s overall percentile rating, grouped by research area (✗ marks the area median). dashboard →

What we’ve evaluated — 57 packages by area

Global health & wellbeing15

Development & governance10

Economics, welfare & policy7

Environment & climate6

Meta-science & methods6

Animal welfare & markets5

Catastrophic & long-term risk4

AI & emerging tech2

Behaviour & attitudes2

Published packages (n=57). Health, development, environment & applied micro — Exeter’s wheelhouse.

A concrete example: an award-winning evaluation

2024–25 Evaluator Prize · 1st Evaluation of “Water Treatment & Child Mortality: a meta-analysis”

“Very influential.”— GiveWell water team (Teryn Mattox); they had been weighing commissioning their own replication. The eval informs chlorination grantmaking.

“Thorough and thoughtful… extensive write-up and precise recommendations.”— the paper’s authors, who revised the framing in response.

Read it →

Do authors find it useful?

Across tracked evaluations

19 of 57 tracked evaluations drew an author response (16 formal)
Of 22 closely assessed: 15 a positive signal; ~a third substantively revised
For 8 papers we compared drafts — a median ~22% of changes traced to our feedback (LLM-assisted)
Author survey (n≈8): quality 30–90; one — “as good as a standard referee report, or better.”

Author responses · author survey

Did authors adapt? All 57, tracked

Each square = 1 of 57 tracked papers, by combined evidence tier. Green = LLM-analysed, shaded by share of major post-evaluation changes attributed to our feedback · blue = manually-confirmed update · orange = mixed / weak signal · grey = not yet assessed. LLM attribution via Claude Opus 4.6 — indicative, human verification ongoing.

The people behind The Unjournal

Our management team (7) runs the process and sets standards; field specialists (~60) source and prioritise research:

David ReinsteinFounder · Co-Director

Anirudh TagatCo-Director

Gavin TaylorManagement

Bob KubinecManagement

Hansika KapoorManagement

Ryan BriggsManagement

Alexander HerwixManagement

The management team (7) · the advisory board (16) and field-specialist teams follow ↓

Each paper is evaluated by ~2 domain experts, often matched from our evaluator pool:

180+ evaluators in the pool

½+ are economists

½+ hold doctorates

40+ field specialists · 8 areas

Full team → · Explore the evaluator pool →

↓ the advisory board · field-specialist teams

The advisory board

Our advisory board — methodologists, forecasters & meta-science researchers across economics, statistics, and policy.

Field-specialist teams (8 areas)

Development economics Anirudh Tagat · Ryan Briggs · Michael Wiebe · Nathan Fiala · Emmanuel Orkoh · Robert Kubinec · Masyhur Hilmy · Wayne Sandholtz · Lee Crawfurd · Yannick Dupraz · Leena Bhattacharya · William Seitz

Global health & well-being Jake Eaton · Rosie Bettle · Charlotte Lane · Shobhit Kulshreshtha · Jonah Goldberg · Valentin Klotzbücher · Priya Lall · Francesco Ramponi · Sarah Reynolds

Economics, welfare & governance David Reinstein · Julian Jamison · Tabaré Capitán · Joel Christoph · Andrei Potlogea · Greg Sasso · Brian Weber · Daniel Horn · Moritz Hennecke · Seth Benzell

Psychology, behavioral science, attitudes Hansika Kapoor · Jonathan Berman · Mattie Toma · Carina Ines Hausladen · Hannah Metzler

Innovation, meta-science, social impact of technology Daniela Cialfi · Jordan Dworkin · Kris Gulati · Andrew Kao · Gavin Taylor · Gary McDowell

Environmental economics Tanya O’Garra · Ben Balmford

Animal welfare (markets, attitudes) Josh Tasoff · Kevin Kuruc · Florian Habermacher · Nicolas Treich · Ash Mader · Brinda Poojary

Catastrophic risks, AI governance & safety David Manheim · Anca Hanea · Alexander Herwix · Tristan Williams

These specialists span many universities and institutions worldwide — a good chance some are already in your department. Two — Julian Jamison and Ben Balmford — are here at Exeter.

4 · Pivotal Questions

The Pivotal Questions project

From single papers → identifying stakeholders’ specific ‘operationalized’ questions that matter:

What would change key decisions, and what research evidence informs this?
What do experts believe now — and how uncertain?
Researchers + Practitioners + Stakeholders
Including Founders Pledge & Animal Charity Evaluators; participants from Coefficient Giving & more

Beliefs on our platforms · overview · workshops: cultured-meat · wellbeing

How a Pivotal Question works

A stakeholder’s decision-relevant question → curated evidence → expert evaluation, with a parallel experts-&-practitioner workshop → synthesis & public output. overview →

What 10 workshop forecasts looked like

n = 10 expert forecasts from our cultured-meat workshop — medians with 80% credible intervals.

Voices from the workshops

Cultured-meat workshop — Oana Kubinyecz on cell-line cost drivers (top). Wellbeing workshop — Matt Lerner’s DALY ↔︎ life-satisfaction comparison & Michael Plant (HLI) on imperfect-but-usable metrics (bottom).

5 · Would this be useful to you?

Exeter people are already involved

Julian Jamison (GPI / Exeter) — field specialist & Pivotal Questions advisor
Ben Balmford (LEEP, Exeter) — environmental-economics field specialist
We’ve published an evaluation of “How Effective Is (More) Money?…” — co-authored by Jamison & Oliver Hauser (Exeter). Package →
And the idea has roots in conversations on this campus

↓ where Exeter economics strengths might fit

Not cold outreach, and not an institutional ask. People here are already involved; if Julian or Ben are in the room, acknowledge them — Julian’s already a field specialist and Pivotal-Questions advisor, Ben’s an environmental-economics field specialist, and we’ve evaluated a Jamison–Hauser co-authored paper. So this is “deepen something real,” not “recruit.” I’m interested in whether any of these connections are useful to people here.

One light, optional connection point if it comes up naturally: Exeter runs the Exeter Prize for behavioural/experimental work, and we run our own recognition — a $6,500 evaluator-prize pool (2024–25) plus author-recognition awards, with monetary author prizes planned. A culture that already prizes judged research is a culture that gets what we’re doing. Don’t push it; it’s a touchpoint, not an ask.

Alignment with Exeter’s strengths

Exeter strength	…maps onto Unjournal work
Behavioural & experimental — Hauser, Fonseca, Balafoutas	belief elicitation; the Pivotal-Questions forecasts
Environmental (LEEP) — Bateman, Groom, Day	natural-capital valuation; our climate & animal-welfare evaluations
Health, wellbeing & development — Jamison, Medina-Lara	cost-effectiveness, WELLBYs, the RCTs we prioritise
Econometrics & methods — Clarke	meta-science; calibrating our ratings
Open research & reproducibility — Kripfganz	public disciplinary judgement alongside repositories & compliance
Development — Roy, Dyer, R. Banerjee	the field RCTs we prioritise
AI, technological change & labour — Hauser (IDSAI), Digital Economy	AI’s societal & labour-market impact — a fast-growing Unjournal priority

Capability-based, not status claims. A few verified specifics if useful in the room: - Behavioural & experimental: the group’s own page calls it “one of the largest groups of behavioural and experimental economists in the world” — theory + experiments (FEELE lab and field) to aid policy design. It’s also home of the Exeter Prize (best publication in behavioural/experimental economics & decision theory). Belief elicitation and policy-relevant experiments map straight onto our Pivotal-Questions forecasts. - LEEP: explicitly evidence-based environmental decision-making — 300+ partners, work that has shaped UK environmental & agricultural policy and embedded environmental value into HM Treasury guidance. That’s the same demand-side logic our evaluations and PQs serve. - Open research is the strongest, lowest-friction fit and I want to draw it out: Sebastian Kripfganz is Exeter Economics’ Open Research Champion (UKRN member institution; Research Methods Centre; data-access-statement / ORE culture). The Unjournal isn’t another repository or compliance layer — it’s public disciplinary judgement that complements them. If the open-research session is on the agenda, that’s the natural conversation. - If Loukas Balafoutas (Head of Department) or Joerg Weber (Director of Research & Impact for Economics; REF2029 coordinator) are in the room, those are the people who’d think about how a public evaluation counts as impact/esteem evidence — but that’s their call, not an ask. - AI & technological change is a fast-growing Unjournal priority — the speed argument, the flood of AI-generated papers, and AI’s labour-market effects. The natural Exeter bridge is Oliver Hauser: Professor of Economics and Deputy Director of Exeter’s Institute for Data Science & AI (IDSAI), advising No.10 / Cabinet Office / HM Treasury on AI use in government — plus the Business School’s “Technological Transformation” theme (Digital Economy: Alan Brown, Mark Thompson). Hauser is already linked to us (the evaluated cash-transfer paper), so it’s not a cold ask.

Name only the people you actually recognise; verify titles can drift. If this feels too generic on the day, it’s safe to skip.

Ways to engage and adopt this

Join our team or evaluator pool
Suggest research (and pivotal questions)
Use our outputs & data
Bring students in
Recognise better signals

Evaluate — paid

Staff · postdocs · advanced PhDs

Paid (~$450 avg) for work you partly do already
Faster & more visible than a report that vanishes into a journal
Named or anonymous; counts as service; citable (DOI)

A referee report you’re proud of becomes a public, citable output.

Submit or suggest research

Authors

Submit a working paper → credible public evaluations + ratings
The journal path stays open — get feedback & a public signal before it resolves
Suggest others’ high-impact work (anonymously if you like)

Why request public evaluation?

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

From our author-reluctance model: selecting into evaluation already shifts beliefs (p0 → p_D — a separating signal); a favourable public evaluation then clears the gatekeeper threshold c. interactive version → · ↓ embedded

Frame it as a commitment and a signal, not a “test.” If strong authors are more willing than weak authors to invite open scrutiny, the act of requesting carries information even before the ratings arrive — and in a screening/sorting equilibrium that commitment can itself become a positive signal. Then the evaluation adds structured evidence: reports, ratings, uncertainty, an author response.

The diagram (from our reluctance model): start at the raw prior p0; selecting into evaluation already shifts beliefs to p_D — only fairly-confident authors opt in, so the act of requesting is itself a separating signal. A favourable public evaluation then pushes the posterior above the gatekeeper threshold c (an adverse one below). Net: the value is highest for strong-but-under-credited work sitting near the bar. Live interactive version: unjournal-reluctance-note.netlify.app/#interactive-model-public-test-after-selection-into-evaluation (also embedded on the next vertical slide).

Try the model live

Interactive: adjust the prior, the selection effect, and the evaluation’s informativeness. open in a new tab → (if the embed doesn’t load)

Evaluation unlocks credibility — wherever you’re from

A Harvard paper? Already trusted. A strong paper from Exeter or Ball State? Unjournal evaluation opens the chest — public, structured evidence that travels independently of institutional prestige.

When is requesting worth it?

Most valuable when your work is strong but under-credited — or sitting just ‘below the bar’
If a commitment to open evaluation becomes a positive signal, you’ll want in early
May be less of a clear win when the work already clears the bar and it’s a sensitive career moment
Timing concerns? Talk to us — we can embargo or schedule

Full “model” (v. preliminary, ~Fable-generated with human feedback): unjournal-reluctance-note.netlify.app

Students & early-career researchers

See what economists, funders & practitioners actually care about: a methodological conversation that sharpens your own work and professional understanding
Get involved with real peer review; get feedback on your evaluation from us, and often from the authors
Visibility within a network of funders, grantmakers & impact-minded researchers
Potential RA / fellowship opportunities: evaluation, meta-research analysis, Pivotal-Questions project support

Use our outputs

Evaluation packages & prioritisation outputs → a vetted evidence base to build on, teach, cite, discuss, and engage with
Pivotal Questions / workshops → framing for agendas, grants, collaborations, REF Impact?
Public evaluations → possible REF / grant / esteem evidence?
The ratings dataset → meta-analysis; Open to field experiment collaborations on the evaluation process

Visibility to research users

Funders & nonprofits read these evaluations
Some use them in grantmaking and methodology
A route to feedback, uptake, and sometimes collaboration

A way to put careful work in front of people who actually use evidence.

Recognise better signals

A strong public evaluation is evidence of quality and usefulness — not just venue: multidimensional ratings (with uncertainty) + expert reports & discussion + an author response + a citable DOI.

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).

6 · Looking ahead

AI makes evaluation more important

A flood of plausible papers generated by AI tools, that may or may not be correct or useful
More need for efficient, transparent evaluation — and connection to real stakeholders & impact
Scalable code / data checks
Current ~consensus: keep a human in the loop for the final decisions and vetting

Not “does it fit a top-5 template” — but “is it true, and does it matter?”

How does AI evaluation compare to humans?

One exploratory pilot · ~45 papers

“Frontier” (Jan. 2026) LLM vs. our human ratings: modest rank agreement (r ≈ 0.3)
Human–human agreement still exceeds human–LLM
On written critiques: LLMs catch ~¾ of human concerns, but ~half their flags aren’t substantive
Not yet a substitute? But it’s an open question.
We’re further exploring AI prioritization, research reasoning, and alignment in this domain

Preliminary methods & results: llm-uj-research-eval.netlify.app/methods

Questions for you

What would make an evaluation count as evidence of quality — reliable, meaningful, valued?
Where would faster public evaluation be most useful?
What would make public evaluation feel safe and valuable for authors?
How could this invigorate teaching & research training?
How could it help build agendas, attract funding, and demonstrate value (e.g. REF)?
Which Exeter strengths connect most naturally?

Thank you

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?

David Reinstein · contact@unjournal.org · unjournal.org · unjournal.pubpub.org

Pacing / structure issues

Spent too long on “why journal peer review is broken” / its limitations — crowded out time for “what we do”
The lengthy diagnosis generated a lot of defensive reactions (“journals provide more than just a stamp”) — audience pushed back on that framing
ACTION: Trim or move the journal-critique section; lead earlier with what UJ actually does

Framing to fix

Remove or soften the “just a stamp” language — it reads as dismissive and triggered audience friction
Funders and practitioners noted they don’t necessarily know what the highest-priority research areas are — address this more explicitly (UJ helps surface that)
Researchers noted they want independence from funder direction — acknowledge this tension honestly

Audience asked process questions that were answered in later slides — had to jump around
ACTION: Move “what is an evaluation and what are the ratings” earlier in the deck (currently too far back)

Audience questions / suggestions worth acting on

“Who are the evaluators?” — came up prominently; needs a stronger early answer
Ben Zarankin: consider whether evaluators should specialize in the evaluator role more explicitly — worth exploring / referencing in the deck

For the next iteration — DONE, ported to generic-talk.qmd (19 June 2026)

These lessons have now been incorporated into generic-talk.qmd (the maintained, reusable deck — live at https://uj-talk.netlify.app):

Softened the “just a stamp” framing — now explicitly grants journals provide more (curation, dissemination, community, trusted signal) before narrowing to “the evaluation is the scarce part”
Compressed the journal-critique line (“PDF prison” demoted to a bonus vertical slide)
Moved “What an evaluation gives you” + “Inside the evaluation form” up to the front of §2 (right after “We are not a journal”) so the ratings/eval content lands early
Surfaced “who evaluates” on the “How it works” slide (180+ pool, ½ doctorates, ~40 professors)
Added funder/practitioner-don’t-know-the-priorities + researcher-independence framing to “Why this can work now”
Recorded Ben Z’s evaluator-specialisation suggestion in the team-slide speaker notes
Ported these Exeter-only slides into generic: “How long does it take?”, “How the team votes”, “Try the model live” (iframe), “Evaluation unlocks credibility” (chest), “Overall ratings by research area”

This Exeter deck is now frozen; the generic deck is canonical going forward. –>