Michael G.

Key Skills

Software

Don't disclose

Top Subject Matter

Historical and ancient-script research

AI response groundedness and response quality evaluation.

AI-assisted education content quality assurance and evidence-aligned curriculum materials.

Top Data Types

Text

Top Task Types

Text Generation

Evaluation/Rating

RLHF

Fine-tuning

Freelancer Overview

Echoes of the Script OpenLab — Independent Researcher / AI Evaluation Lead. Brings 23+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Zenodo, GitHub documentation, and Don't disclose. Education includes Bachelor of Arts, Wingate University (2013). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Text Generation.

Labeling Experience

Echoes of the Script OpenLab — Independent Researcher / AI Evaluation Lead

Text

Evaluated AI-generated historical and research outputs by checking factual grounding, missing evidence, chronology errors, unsupported comparisons, overclaiming, and logical weakness. Applied traffic-light accountability labels (green/yellow/red) based on whether evidence supports claims and kept AI output as logged input rather than authority. Produced structured reviewer outcomes and confidence ratings for peer-auditable verification workflows. • Traffic-light labeling of claim support status • Evidence-alignment checks and error-mode tagging (unsupported/misleading/overconfident) • Rubric-style scoring with confidence ratings and R1/R2 reviewer outcomes • Claim-level logging using a falsifiability/claim ledger workflow

2024 - Present

Independent Home Educator — Curriculum Designer & AI Content QA

Don't discloseTextText Generation

Reviewed and revised AI-assisted educational drafts to ensure factual accuracy, clarity, grade-level fit, and alignment with supporting evidence. Used diagnostics and error logs to track issues and improve learner performance by correcting recurring reasoning gaps. Applied structured feedback to improve the quality of AI-assisted educational content. • Evidence-alignment validation for AI-generated educational text • Quality checks for accuracy and clarity • Tracking recurring reasoning gaps via diagnostics/error logs • Iterative revision of AI-assisted drafts for learner suitability

2020 - 2025

Education

Work History