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✓ Certified ✓ Refereed ★ Canon P published also in Quantitative Science Studies

The Crisis in Academic Publishing: Symptoms, Diagnoses, and Potential Solution Concepts

Gérard P. CachoniD, Christian TerwieschiD, Karl T. UlrichiD (corresponding)

The Wharton School, University of Pennsylvania · Operations, Information and Decisions

Ledger ID aldg:2026.0427  ·  DOI https://doi.org/10.59312/aldg.2026.0427  ·  v2, revised 28 May 2026  ·  CC BY 4.0

Quality on the academic Ledger
QaL
90 96 99
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.

JEL A14JEL I23JEL O31JEL D80

Keywords: scholarly publishing · peer review · evaluation · inter-rater reliability · citation dynamics · large language models · mechanism design

Inside the QaL estimate — the signals behind it

QaL 96 90–99
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.

Submission history

v228 May 2026Added Section 5 (stylized two-stage model); expanded reliability evidence. diff
v112 Mar 2026Initial submission — certified within 2 days. diff

Comments: 28 pages, 5 figures. Seminar version prepared for the OID Department, 9 July 2026.

Status

Certified · 14 Mar 2026
ORCID-authenticated, screened, archived
Refereed · 4 Jun 2026
5 signed reviews, 27 endorsements
Canon · 2 May 2027
conferred by field panel
Published version
Quantitative Science Studies 7(2) (2026) — Ⓟ

Identifiers

Ledger ID  aldg:2026.0427
DOI  10.59312/aldg.2026.0427
Version  v2 (current)
License  CC BY 4.0
Language  English

Classification

Primary field  Operations & Information
Secondary  Economics of science
A14I23O31D80
JEL codes · OpenAlex topics auto-tagged

Integrity & openness

Data & code  repository ↗ (DOI-linked)
Funding  Mack Institute (Wharton)
Conflicts  None declared
AI collaboration  Integral — ideation, literature synthesis, analysis, and drafting; all outputs author-verified
Ethics / IRB  N/A (no human subjects)