eventual percentile · point estimate with 90% interval
Field Decision Sciences / Economics of Science JEL A14 · I23 · O31 Vintage 2026 cohort · benchmarked within field × year (n = 412)
0%
<50
1%
50–75
3%
75–90
18%
90–95
55%
95–99
23%
99–100
probability of landing in each NSF percentile class (top 50 / 25 / 10 / 5 / 1%)
0.89
P(Top 10%)
forecast, field × cohort
142
Citations
97th pct in cohort
27
Endorsements
named, ORCID-verified
18.4k
Views
6.2k full-text / PDF
Abstract
Academic publishing bundles four functions — administration, certification, development, and recognition — into a single, slow, gatekeeping step. We document the resulting failures: two or three referees cannot reliably judge importance (inter-rater reliability near 0.3); submission volume is surging under generative AI; and the signals that actually distinguish important work emerge only over years. We argue the crux move is to decouple certification from recognition and to break the constraint of simultaneity. We describe the Academic Ledger, a neutral non-profit record in which work is certified up front and importance accrues over time through measured impact and named, signed endorsement, organized in three tiers — Certified, Refereed, and Canon — and we sketch a stylized two-stage model that makes a low early entry bar optimal under noisy signals and heavy-tailed importance.
0.93 probability this paper is a top-20% paper in its field-and-year cohort
97th percentile among the 412 papers in its cohort · momentum ▲ rising · last updated 2 Jun 2026
Forecast is model-based with uncertainty; the band narrows as impact realizes.
Citations (field-normalized) 30%
88
Named endorsements 20%
82
Expert panel rating 15%
92
Usage (views, downloads) 15%
79
Cross-field reach 10%
71
Discussion & engagement 10%
85
Weights are tunable and public; the score is reproducible from the raw signals below. No single number confers Canon — a field panel does, under published criteria, with this dossier in view.
All metrics
Attention & reach
Abstract views18,432
Full-text / PDF downloads6,210
Inbound links (syllabi, blogs, news)53
News & media mentions11
Social posts (X, Bluesky, Mastodon)340
Policy / institutional citations4
Reach (countries / institutions)47 / 213
Altmetric attention score128
Engagement
Likes / saves410
Followers96
Comments89
Unique discussants64
Use in teaching (syllabus inclusions)22
Use in practice (industry / policy)7
Endorsement & expert judgment
Named endorsements (ORCID-verified)27
Signed reviews5
Expert panel rating4.6 / 5
Community reader rating4.4 / 5 (n=83)
Scholarly impact
Citations (total)142
Citations, last 12 mo.61
Field-weighted citation impact7.8×
Relative citation ratio (RCR)5.2
Authority-weighted (PageRank)93rd pct
Cross-field citation share38%
Disruption index (CD₅)+0.31
Replications (successful / attempted)2 / 2
Citations by year
cumulative · 2026 → 2026 (illustrative); still rising
Endorsements — 27 named, signed, on the record
LM
L. Marchetti · iD verified
Professor of Economics, Bocconi · endorsed 14 Apr 2026
"The clearest articulation yet of why certification and recognition should be unbundled."
RO
R. Okafor · iD verified
Associate Professor of Management, LBS · endorsed 2 May 2026
"The reliability evidence alone should change how committees read CVs."
+25
25 more endorsers
across economics, operations, sociology, and information systems
Discussion — 89 comments · 64 discussants
A. Novak · Stanford · 3 wk ago
Does the two-stage model assume the importance signal is stationary? Sleeping beauties seem to violate that.
K. Ulrich· author · 3 wk ago
It doesn't require stationarity — late Canon review is precisely what catches the late bloomers.
J. Park · MIT · 1 mo ago
Worth contrasting the ensemble-LLM evidence with the single-reviewer baseline more explicitly.