coulomb-research/seed/ResearchCharts.md

Research Charts

Purpose

Research Charts make scientific activity culturally legible and discoverable.

They borrow from music charts, box-office rankings, and certification systems while refusing to equate popularity with truth.

The purpose is relevance marketing, discovery, recognition, and system sensing.

1. Core Principle

Charts rank observable signals within a declared scope. They do not rank truth.

Every chart must make clear:

• what is measured,
• over what time period,
• within what comparison cohort,
• with what normalization,
• using which data sources.

2. Chartable Objects

Primary chart targets:

• papers,
• propositions,
• datasets,
• software,
• replications,
• criticisms,
• open problems,
• research programs,
• research collectives.

Person-centric rankings should be secondary.

The system should reward useful research objects and contributions before celebrity.

3. Initial Chart Families

General

• Global Research Top 100
• Research Top 100 — Year
• Research Top 100 — YTD
• Research Top 100 — Month

Field-specific

• AI Top 50
• Physics Top 50
• Biology Top 50
• Systems Top 50
• field-defined charts

Dynamic

• Fastest Rising
• Breakthrough Movers
• Long-Tail Classics
• Comeback Papers

Scientific-function charts

• Most Reused
• Most Replicated
• Most Challenged
• Most Generative
• Most Discussed
• Most Influential Methods
• Most Productive Open Problems
• Most Valuable Criticisms
• Replication of the Year
• Most Useful Negative Result

These categories deliberately broaden prestige beyond publication.

4. Time Windows

Each chart should support explicit windows such as:

• day,
• week,
• month,
• quarter,
• year-to-date,
• calendar year,
• rolling 12 months,
• all-time.

Velocity charts should be distinct from accumulated-impact charts.

5. Normalization

Research fields differ dramatically in publication volume, citation behavior, and attention patterns.

Possible normalization dimensions:

• field,
• year,
• age,
• artifact type,
• size of active research community.

Preferred representation:

percentile within field + cohort + period

rather than arbitrary universal raw thresholds.

6. Certifications

Coulomb may recognize durable observable influence through certification tiers.

Candidate labels:

• Bronze
• Silver
• Gold
• Platinum
• Diamond

Certification does not mean:

• scientifically true,
• endorsed by Coulomb,
• free of criticism,
• superior in all dimensions.

It means only that a declared observable threshold has been crossed.

7. Multi-Dimensional Certification

Avoid one universal certification.

Possible forms:

Gold — Reach
Gold — Reuse
Gold — Replication
Gold — Generativity
Gold — Influence

Example artifact profile:

PLATINUM — Reuse
GOLD — Generativity
SILVER — Reach
Support — Contested

This communicates more than one compressed prestige score.

8. Threshold Models

Possible threshold approaches:

Absolute

Example:

1,000 verified downstream reuses

Easy to understand, but poor across fields.

Percentile

Example:

Gold     = top 1% within field/year cohort
Platinum = top 0.1%
Diamond  = top 0.01%

Numbers above are illustrative, not normative.

Percentile-based systems are likely better for heterogeneous research domains.

Hybrid

Require:

• minimum absolute signal,
• minimum age,
• and percentile threshold.

This may reduce distortion in very small fields.

9. Research Collectives

A research collective may contain:

• humans,
• agents,
• organizations,
• temporary collaborators.

Collectives may accumulate portfolio recognition through their artifacts.

Example:

Coulomb Intelligence Research

2× Platinum
5× Gold
11× Silver

Top 100 appearances: 27

This supports identity and community formation without making individual academic rank the primary prestige unit.

10. Agent Contributions

Charts should preserve mixed human-agent contribution provenance.

Recognition may be attributed to:

• artifact,
• collective,
• contributing actors,
• agent lineage,
• research program.

The system should avoid pretending that only humans contributed where agents performed material work.

11. Relevance Marketing

Research Charts provide a public interface that answers questions such as:

• What research is moving right now?
• Which old work is unexpectedly becoming relevant again?
• Which paper is being replicated most?
• Which research artifact is generating the most follow-up work?
• Which criticism changed an active research program?
• Which open problems are attracting new resources?

This makes research easier to encounter outside conventional academic discovery channels.

12. Cybernetic Role

Charts are sensors on the research system.

Possible automated responses:

unusual attention spike
  → deploy Critic daemon

rapid reuse
  → schedule replication review

high dependency centrality
  → perform provenance audit

high controversy
  → generate competing-model synthesis

high generativity
  → study successful research pattern

Public relevance signals can therefore feed directly back into research quality.

13. Goodhart Resistance

Anti-gaming measures should include:

• transparent metric definitions,
• multiple dimensions,
• rotating or evolving chart functions,
• field normalization,
• verified provenance,
• separation of attention from support,
• independent recomputability,
• anomaly detection,
• no hidden master score.

Chart algorithms themselves should be versioned research objects.

14. Design Maxim

Celebrate what moves research forward, not merely what accumulates attention.