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BEGINNING Scale

Overview

The BEGINNING Scale, developed in 2004, is an innovative psychological and analytical tool designed to address the challenge of choosing suitable academic specializations.

 

It identifies individuals' psychological and cognitive inclinations through symbolic three-letter codes, each representing a unique combination of leadership style, emotional and social intelligence, core strengths, and recommended hobbies. These codes are translated into algorithmic models to predict relative productivity potential in academic and professional fields.

Definition of the Scale

BEGINNING is a symbolic analytical tool that explores the foundations of intellectual, behavioral, and societal excellence. It classifies individuals into 120 unique three-letter codes, each representing specific traits, capabilities, and inclinations. The model provides a framework for constructing relative cognitive awareness and modeling how individuals process and develop their internal potential.

Objectives

  • Educational and psychological guidance for students.

  • Human resource development and career counseling.

  • Academic research in behavior and productivity.

  • Future integration into AI modeling and artificial consciousness design.

Core Analytical Components

Leadership & Drive

Drive: Determines whether the individual is a natural leader, innovator, organizer, cautious planner, or motivator

Best-Suited Hobby

Suggests practical, mental, artistic, or social hobbies based on the individual’s profile.

Academic & Professional Fit

Assesses relative productivity potential in academic and career paths

Core Strengths

Identifies excellence in analytical thinking, creativity, executive skills, leadership, etc

Emotional & Social Intelligence

Measures traits such as sociability, collaboration, rationality, and emotional independence.

Applications

  • Educational and psychological guidance for students.

  • Human resource development and career counseling.

  • Academic research in behavior and productivity.

  • Future integration into AI modeling and artificial consciousness design.

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