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The 7D Candidate Evaluation Framework

An open methodology for multi-dimensional candidate evaluation that finds exceptional talent traditional systems miss

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Open Source·CC BY 4.0 License

The Problem with Traditional Screening

Keyword-based Applicant Tracking Systems (ATS) systematically reject exceptional candidates who:

  • Are self-taught without formal degrees
  • Changed careers with transferable skills
  • Have entrepreneurial experience ("employment gaps")
  • Lack buzzword density despite strong capability

The 7D Framework Solution

Multi-dimensional evaluation across seven independent dimensions, each scored 1-10:

1. Qualification Match
Skills/experience alignment with requirements
2. Capability Confidence
Certainty they can perform the role
3. Situational Stability
Likelihood to join, stay, and be available
4. Reward Potential
Upside value if this hire succeeds
5. Cultural Fit
Work style and values alignment
6. Career Trajectory
Growth pattern and learning velocity
7. Compensation Alignment
Salary expectation fit
Aggregate Score: 0-100
Weighted combination (role-dependent)

Hidden Gem Detection

Identifies exceptional candidates with high capability and potential who traditional systems would reject:

Example: Self-Taught Developer

  • High school education, 6 years self-taught
  • Portfolio: 3 production SaaS apps, 8k+ users
  • 7D Score: 80/100 (Hidden Gem: Yes)
  • ATS: Auto-reject (lacks degree requirement)

Detection Signals

  • High Capability (7+) + Low Qualification (≤6)
  • High Reward Potential (8+) + Non-linear career
  • High Trajectory (8+) + Short experience
  • Portfolio evidence contradicts credential gaps

Framework Documentation

Specification

Complete framework documentation with dimension definitions, scoring guidance, and bias mitigation

Implementation Guide

Manual evaluation worksheets for recruiters and software integration guide for developers

Schemas

Machine-readable YAML/JSON schemas with dimension specs and output format definitions

Examples

Sample evaluations showing traditional candidates, hidden gems, and comparison scenarios

License

CC BY 4.0 - Free to share, adapt, and use commercially with attribution

Changelog

Version history and framework evolution

Predicting Success in the AI Era

The 7D Framework is built on decades of organizational psychology research showing that judgment and initiative are stronger predictors of long-term success than credentials alone.

"In a world where AI can automate technical tasks, the real differentiators are human judgment, adaptability, and the capacity to navigate ambiguity."

— Based on research from organizational behavior studies (Schmidt & Hunter, 1998; Barrick & Mount, 1991)

Skills (Execution): Can they do the job today? (Qualifications + Capability)
Judgment (Decision-Making): Will they make good decisions? (Values fit + Realistic expectations)
Initiative & Adaptability: Will they grow with the role? (Learning velocity + Career trajectory)

Why this matters now: As AI automates routine work, hiring for judgment and adaptability—not just credentials—becomes critical. The 7D Framework systematically evaluates what AI can't replace.

Who Uses the 7D Framework?

Recruiters

Manual evaluation using worksheets and scoring guidance. 10-15 minutes per candidate after calibration.

Learn more

Developers

Automated 7D evaluation via AI-powered scoring. Integrate using schemas, pseudocode, and API documentation.

View integration guide

Researchers

Reference in academic work, build upon for research, adapt for specific domains. CC BY 4.0 licensed.

Citation format

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