Tables & Figures (eursr-tables-figures)

SkillDev tools

Use when building tables and figures for a European Sociological Review (ESR) manuscript. ESR excludes tables and figures from the ~8,000-word count but expects them to be clear, self-contained, and to carry magnitude and uncertainty for a comparative quantitative readership. Designs exhibits; it does not run the analysis.

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Tables & Figures (eursr-tables-figures) skill

What this skill tells your AI

The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in European-Sociological-Review-Skills/skills/eursr-tables-figures/SKILL.md and read by ahel’s review.

Exhibits are where a double-blind reviewer checks whether the comparative or longitudinal result is real. ESR excludes tables and figures from the ~8,000-word count (endnotes and references count) — so a clear exhibit costs nothing against length, but it must earn its place and stand on its own.

When to trigger

  • Designing the main results table/figure or a key descriptive/cross-national exhibit
  • Deciding what belongs in the article vs. the (supplementary) appendix
  • A reviewer found an exhibit unclear, mislabeled, or not self-contained
  • Presenting a cross-level interaction or country-level pattern visually

Principles

  1. Self-contained. Title, row/column labels, and a complete note make each exhibit intelligible alone. State the data source (ESS/EU-SILC/SOEP/EVS or register), sample, N (and number of countries), units, weighting, estimator, and what the estimate is.
  2. Show magnitude and uncertainty. Effect sizes and intervals — not stars alone. Predicted- probability and marginal-effects plots usually beat dense coefficient walls for a comparative reader, and coefficient/forest plots convey cross-country variation better than tables.
  3. Comparative & longitudinal exhibits. Country forest plots / caterpillar plots of random effects; interaction plots showing the cross-level moderation; mobility/transition tables; survival curves and cumulative-incidence plots for event history; growth-trajectory plots for panels.
  4. Accessible. Colorblind-safe palettes; legible in grayscale; no chartjunk or 3D.
  5. Reproducible. Generated by the master script so numbers match the deposited replication package (see eursr-transparency-and-data).

Format

  • Follow OUP/ESR table and figure conventions; concise notes carrying all interpretive detail.
  • Keep identifying information out of exhibits (double-blind review).
  • Tables and figures are excluded from the ~8,000-word limit but must still be necessary and clear.

Exhibit conventions a double-blind ESR reviewer expects

Result typeWorkhorse exhibitThe note must state
Cross-national effectcountry forest/caterpillar plotdata, N, # countries, estimator
Cross-level interactionpredicted-margins interaction plotwhat is held constant, CI basis
Mobility / attainmenttransition / mobility tableorigin–destination coding, N
Event historysurvival / cumulative-incidence curverisk set, time scale, censoring
Panel / growthtrajectory plot with CIswithin vs. between, attrition handling

Worked micro-example (illustrative)

A main results table for a 24-country scarring study is redesigned for ESR.

Before: 6 columns of multilevel coefficients, stars only, no note → reviewer can't tell magnitude,
  sample, or number of countries
After: (1) a marginal-effects interaction plot — scar shrinks as activation spending rises (CI band);
  (2) a country caterpillar plot of random slopes showing where the effect is strong/weak
Self-contained note (illustrative): "Predicted within-person wage penalty from a two-level model,
  N = 86,000 in 24 countries, survey-weighted; bands are 95% CIs (wild cluster bootstrap);
  reproduces from master.R, seed = 2026."

The redesign stands alone, leads with the substantive magnitude and the cross-national variation, and ties the numbers to the deposited script.

Referee pushback → ESR-specific fix

  • "I can't read this table without the text." → Add a complete note (source, N, # countries, units, weighting, estimator) so it is intelligible alone.
  • "Stars don't tell me if this matters." → Replace with marginal effects and intervals; for a comparative claim, show the country-level spread with a forest/caterpillar plot.
  • "Where is the cross-level interaction?" → Plot predicted margins across the macro variable rather than reporting a bare interaction coefficient.
  • "Your figure dies in grayscale." → Re-encode with colorblind-safe, grayscale-legible channels.

Calibration anchors

  • Exhibits are excluded from the word cap — use the room. ESR counts endnotes and references but not tables/figures, so a clear, self-contained exhibit costs nothing against length.
  • Show the comparison. A country forest/caterpillar plot communicates cross-national variation a generalist can grasp far better than a coefficient column.
  • Numbers must match the replication package. Exhibit values that disagree with the deposited code read as a credibility failure under ESR's replication mandate.

Execution bridge (StatsPAI / Stata MCP)

Generate exhibits from the fitted result, not by retyping numbers (the usual source of body-vs-supplement drift). Full map: execution-with-mcp. ESR is comparative quantitative sociology; cross-country panels with confounded institutions — foreground fixed effects and clustering.

  • Tables: etable (multi-model columns) or did_summary_to_latex straight from the result_id.
  • Figures: plot_from_result / enhanced_event_study_plot / event_study_table — axis units and the SE/clustering note baked in.
  • Every note names the estimator + clustering and states the effect size in interpretable units.

See a full fitted-result → exhibit chain in the JF execution walkthrough.

Anti-patterns

  • Tables that require the prose to be intelligible (not self-contained)
  • Significance stars with no effect size or interval
  • Reporting a bare cross-level interaction coefficient with no predicted-margins plot
  • Color-only encoding that fails in grayscale or for colorblind readers
  • Exhibit values that don't match the analysis script / replication package

Output format

【Main exhibit】what it shows + why a table/figure
【Self-contained?】title + labels + note + source/N/# countries/weights present? [Y/N]
【Magnitude + uncertainty shown?】[Y/N]
【Comparative variation shown?】(forest/interaction plot where relevant) [Y/N]
【Reproducible?】matches master script / replication package? [Y/N]
【Next】eursr-writing-style

Supplementary resources

Signals

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eursr-tables-figures
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github.com/brycewang-stanford/awesome-journal-skills