Everyone’s Name on Every Paper

Metascience
Statistics
Research Integrity
Author

Ravi Kalia

Published

August 14, 2026

Everyone’s Name on Every Paper

Paper and citation counts credit every name on the author list. They do not measure work done.

1 Author-list pooling

Setup: \(n\) researchers, each with one solo paper and \(c_i\) citations. They put all \(n\) names on every paper. The papers are unchanged; only the lists change.

Outcomes:

  • Each researcher’s paper count: \(1 \to n\).
  • Each researcher’s citation count: \(c_i \to S = \sum_j c_j\).

Multiplier:

\[\frac{S}{c_i} = n \cdot \frac{\bar{c}}{c_i}.\]

  • Papers multiply by exactly \(n\).
  • Citations multiply by more than \(n\) when citation counts have a heavy right tail: \(\bar{c}/c_i > 1\) for most members.

2 Simulated citation counts

The counts below are synthetic. The question is counterfactual — what the same researchers’ totals would be with and without pooling — and no real database holds both branches.

Generating process:

  • Draw \(c_i\) from a lognormal distribution (\(\mu = 1.5\), \(\sigma = 1.2\) on the log scale).
  • Median near 4.5 citations; a few papers in the hundreds.
  • Stands in for one field’s citation distribution.

Downstream use: hiring panels reading CV totals.

Code 
import numpy as np

RNG = np.random.default_rng(0)
MU, SIGMA = 1.5, 1.2          # lognormal, on the log scale
GROUPS = (5, 10, 20)
REPS = 20_000                 # many draws, so the medians are not a fluke of one

print(f"{'n':>3}  {'papers':>7}  {'median':>9}  {'share above':>12}  {'large-n':>8}")
print(f"{'':>3}  {'':>7}  {'citations':>9}  {'n times':>12}  {'limit':>8}")
for n in GROUPS:
    c = RNG.lognormal(MU, SIGMA, size=(REPS, n))
    # Everyone is credited with the group total, so the multiplier is S / c_i.
    mult = c.sum(axis=1, keepdims=True) / c
    limit = n * np.exp(SIGMA**2 / 2)   # what median(S / c_i) tends to as n grows
    print(f"{n:>3}  {n:>6}x  {np.median(mult):>8.1f}x  {np.mean(mult > n):>11.0%}"
          f"  {limit:>7.1f}x")
  n   papers     median   share above   large-n
              citations       n times     limit
  5       5x       7.3x          63%     10.3x
 10      10x      17.2x          67%     20.5x
 20      20x      37.3x          70%     41.1x

Results (20,000 replicates per \(n\)):

  • Median citation multiplier exceeds \(n\) at every group size: 7.3× at \(n = 5\), 37× at \(n = 20\).
  • Most researchers gain more than the \(n×\) paper-count inflation (“share above \(n\) times” column).

3 Unequal redistribution

Pooling rule: every member is credited the group total \(S\), regardless of solo count \(c_i\).

  • Low-\(c_i\) members gain most.
  • High-\(c_i\) members gain least; \(S \le n\,c_{\max}\), so the most-cited member cannot reach \(n×\) their solo count.
  • No one’s credited count falls; relative standing of the top contributor erodes.

The figure below: one draw per group size. Left end = solo citations; right end = pooled total; orange tick = \(n ×\) solo count.

Code 
import matplotlib.pyplot as plt

SOLO, COMBINED, REF = "#6b7280", "#4a3aa7", "#eb6834"
rng = np.random.default_rng(1)

fig, axes = plt.subplots(3, 1, figsize=(7.4, 8.2), sharex=True,
                         gridspec_kw={"height_ratios": GROUPS, "hspace": 0.16})

for ax, n in zip(axes, GROUPS):
    c = np.sort(rng.lognormal(MU, SIGMA, size=n))
    total, y = c.sum(), np.arange(n)
    ax.hlines(y, c, total, color=SOLO, lw=1.2, alpha=0.55, zorder=1)
    ax.scatter(c, y, s=34, color=SOLO, zorder=3, label="solo: own paper only")
    ax.scatter(np.full(n, total), y, s=34, color=COMBINED, zorder=3,
               label="combined: credited the group total")
    ax.scatter(n * c, y, s=52, marker="|", color=REF, lw=1.8, zorder=4,
               label=r"$n \times$ the solo count")
    ax.set_xscale("log")
    ax.set_ylim(-0.9, n - 0.1)
    ax.set_yticks([])
    ax.set_ylabel(f"$n = {n}$", fontsize=11)
    ax.grid(axis="x", color="0.9", lw=0.7)
    ax.set_axisbelow(True)
    for side in ("top", "right", "left"):
        ax.spines[side].set_visible(False)
for ax in axes[:-1]:
    ax.spines["bottom"].set_visible(False)
    ax.tick_params(axis="x", which="both", bottom=False)

axes[0].legend(frameon=False, fontsize=9, loc="upper left",
               bbox_to_anchor=(0.0, 1.6))
axes[-1].set_xlabel("credited citations (log scale)", fontsize=10)
plt.show()
Figure 1: One draw per group size. Left ends are what each researcher earned alone; right ends are what all of them are credited with after combining. The orange tick marks n times the solo count: left of the total, that researcher beat n times; right of it, they did worse.

Across 20,000 draws: roughly two thirds of researchers clear \(n×\). The most-cited member never does.

4 Real-world instances

Same arithmetic — pool credit, credit everyone named — at three scales:

Scale Currency exchanged Example
Authors Seniority / reciprocity Honorary authorship: 26% self-reported in health sciences (meta-analysis, 2024); 18% after ICMJE criteria shown; 51% when contributions were checked
Journals Reciprocal citations 46 journal pairs (55 journals) suspended by Journal Citation Reports by 2019; CIDRE detects citation rings
Institutions Cash 60+ highly cited researchers added King Abdulaziz University as paid second affiliation (Science, 2011); university ranked second worldwide on highly cited list by 2014

5 Constraints

  • Teams produce more-cited work than solo authors (Wuchty, Jones and Uzzi, 19.9 million papers). Honest and dishonest incentives both favour adding names.
  • Most author lists reflect real collaboration; the cases above are not the model at full strength.
  • Paper and citation counts cannot distinguish earned from gifted authorship.

6 References