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A model for evaluating how well information aligns with real-world context — in HR, hiring, and beyond.
What is Contextual Relevance Scoring (CRS)?
The Contextual Relevance Scoring (CRS) framework is a system I developed to evaluate the relevance, completeness, accuracy, and timeliness of information — especially in HR decision-making, job matching, employee evaluations, and AI-generated outputs.
Rather than just relying on keywords or surface-level scoring, CRS helps ask:
“How well does this actually fit the situation, the need, or the context at hand?”
CRS is rooted in qualitative synthesis, real-world dialogue analysis (e.g., class discussions, Reddit forums, peer reflections), and AI-assisted pattern recognition.
Step 1
Define the real-world context
(Job role, candidate, user need, etc.)
Step 2
Evaluate content across key dimensions
(Relevance, Accuracy, Completeness, Timeliness)
Step 3
Assign a relevance score with AI support
Scores can guide fit, alignment, or next steps
Where CRS Can Be Applied
Resume-to-Job Fit Scoring
AI-Generated Response Evaluation
Hiring Panel Alignment Check
Peer Review or Essay Evaluation
Employee Feedback Interpretation
Course Discussion Thematic Analysis
What I’ve Learned Using CRS
In analyzing 48 graduate students’ discussion responses, CRS helped surface hidden themes in how students define fairness, effort, and performance — even when scores varied.
CRS has proven especially valuable in highlighting subtle misalignments between what’s said and what’s needed — such as job posts vs. candidate strengths, or AI outputs vs. HR policies.
Using CRS with tools like ChatGPT and NotebookLM accelerates pattern recognition and blind spot detection in large text datasets.
Tools Supporting the CRS Framework
💬 ChatGPT (for response generation and review)
📚 NotebookLM (for consolidation and source comparison)
📈 Custom CSV scoring models (locally run or AI-supported)
🧠 Manual thematic coding + AI augmentation
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