Update README.md

README.md

@@ -1,99 +1,83 @@
----
-title: Cultural Exposure and Algorithmic Risk Model
-emoji: "🧭"
-colorFrom: "blue"
-colorTo: "green"
-sdk: gradio  # THIS IS THE CRITICAL LINE
-app_file: app.py
----
-# Cultural Exposure & Algorithmic Risk (CEAR) Baseline v1.0
-
-## Model Description
-
-The **Cultural Exposure & Algorithmic Risk (CEAR) Model** is an **analytic, rule-based scoring system** designed to help users and researchers interpret social media usage in terms of its potential impact on cultural awareness and algorithmic vulnerability.
-
-This version is a V1 Baseline: it is **deterministic** (theory-driven by fixed rules and weights) and does not rely on supervised machine learning or proprietary user data.
-
-### 🎯 Key Outputs
-
-1.  **Cultural Connectedness Score (C-Score):** Estimates exposure to viral and trending content, modeled with diminishing returns on time.
-2.  **Algorithmic Risk Score (A-Risk):** Quantifies vulnerability incurred from concentrated time on high-intensity, opaque algorithmic feeds.
-3.  **Platform Diversity Index (D-Index):** Measures the concentration/spread of usage across platforms (using $1/\text{HHI}$).
-4.  **Cultural Efficiency:** Per-platform estimates of C-Score gained per minute spent.
-
-## ⚙️ Analytic Basis & Scoring Logic
-
-The model is defined by transparent assumptions encoded in the Python code (`cear_model.py`) and the platform weights (`platform_weights.json`).
-
-### Core Formulas
-
-The key to the C-Score is the **Diminishing Returns Function** ($f_{DR}$), which prevents the C-Score from increasing linearly with time, acknowledging that the first hour is likely more valuable than the tenth.
-
-$$f_{DR}(\text{Min}) = \log_{10}(\text{Min} + 1)$$
-
-The final scores are calculated as:
-
-$$C_{Score} = \sum_{i} \left[ W_{C,i} \times f_{DR}(\text{Min}_i) \right]$$
-
-$$A_{Risk} = \sum_{i} \left[ W_{A,i} \times \text{Min}_i \right]$$
-
-*(Where $W_{C}$ is the Trend Density Weight and $W_{A}$ is the Algorithmic Risk Weight, defined in `platform_weights.json`.)*
-
-## 🚀 Deployment & Usage (Hugging Face Space)
-
-This repository contains the core logic (`cear_model.py`) and the application interface (`app.py`) for a Hugging Face Space.
-
-### Model Integration (The Engine)
-
-The core logic can be imported and run in any environment:
-
-```python
-import pandas as pd
-from cear_model import CEARModel
-
-# Example Input Data
-user_data = pd.DataFrame([
-    {'platform_name': 'TikTok', 'minutes_per_week': 450},
-    {'platform_name': 'YouTube', 'minutes_per_week': 200},
-    {'platform_name': 'Reddit', 'minutes_per_week': 50},
-])
-
-model = CEARModel()
-results = model.calculate_scores(user_data)
-# {'C_Score': 3.75, 'A_Risk': 565.0, ...}
-
-# Application Interface (The App - app.py)
-
-The app.py script uses the Gradio library to create an interactive web interface. It handles:
-
-    Collecting user input via a table component.
-
-    Calling the CEARModel.calculate_scores() method.
-
-    Generating a qualitative natural language summary based on the quadrant of the C-Score and A-Risk (e.g., "High C, Low A").
-
-⚠️ Limitations and Ethical Considerations
-
-1. Theoretical, Not Validated: The scores are based on fixed, theoretical assumptions about platform design. They are not calibrated against real-world user survey data or outcomes (e.g., actual cultural literacy, actual regret). Scores are relative estimates only.
-
-2. No Content Analysis: The model only uses time and platform. It cannot distinguish between a productive hour watching educational content and an unproductive hour scrolling low-quality content.
-
-3. Future Work: This deterministic model serves as a foundation. Future versions are intended to use the same input schema to train supervised machine learning models that directly predict outcomes (e.g., predicting user-reported "felt caught up" or "post-scroll regret").
-
-
----
-
-## 2. `requirements.txt` (For Deployment)
-
-This file lists the necessary Python packages for the Gradio Space to run your model and interface correctly.
-
-```text
-# requirements.txt
-
-# Core Model Dependencies
-pandas
-numpy
-
-# Gradio Space Dependencies
-# Gradio is used to build the simple web application interface (app.py)
-gradio
+---
+title: Cultural Exposure Risk Model
+emoji: 🧭
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 6.0.2
+app_file: app.py
+pinned: false
+---
+# Cultural Exposure & Algorithmic Risk (CEAR) Baseline v1.0
+
+## Model Description
+
+The **Cultural Exposure & Algorithmic Risk (CEAR) Model** is an **analytic, rule-based scoring system** designed to help users and researchers interpret social media usage in terms of its potential impact on cultural awareness and algorithmic vulnerability.
+
+This version is a V1 Baseline: it is **deterministic** (theory-driven by fixed rules and weights) and does not rely on supervised machine learning or proprietary user data.
+
+### 🎯 Key Outputs
+
+1.  **Cultural Connectedness Score (C-Score):** Estimates exposure to viral and trending content, modeled with diminishing returns on time.
+2.  **Algorithmic Risk Score (A-Risk):** Quantifies vulnerability incurred from concentrated time on high-intensity, opaque algorithmic feeds.
+3.  **Platform Diversity Index (D-Index):** Measures the concentration/spread of usage across platforms (using $1/\text{HHI}$).
+4.  **Cultural Efficiency:** Per-platform estimates of C-Score gained per minute spent.
+
+## ⚙️ Analytic Basis & Scoring Logic
+
+The model is defined by transparent assumptions encoded in the Python code (`cear_model.py`) and the platform weights (`platform_weights.json`).
+
+### Core Formulas
+
+The key to the C-Score is the **Diminishing Returns Function** ($f_{DR}$), which prevents the C-Score from increasing linearly with time, acknowledging that the first hour is likely more valuable than the tenth.
+
+$$f_{DR}(\text{Min}) = \log_{10}(\text{Min} + 1)$$
+
+The final scores are calculated as:
+
+$$C_{Score} = \sum_{i} \left[ W_{C,i} \times f_{DR}(\text{Min}_i) \right]$$
+
+$$A_{Risk} = \sum_{i} \left[ W_{A,i} \times \text{Min}_i \right]$$
+
+*(Where $W_{C}$ is the Trend Density Weight and $W_{A}$ is the Algorithmic Risk Weight, defined in `platform_weights.json`.)*
+
+## 🚀 Deployment & Usage (Hugging Face Space)
+
+This repository contains the core logic (`cear_model.py`) and the application interface (`app.py`) for a Hugging Face Space.
+
+### Model Integration (The Engine)
+
+The core logic can be imported and run in any environment:
+
+```python
+import pandas as pd
+from cear_model import CEARModel
+
+# Example Input Data
+user_data = pd.DataFrame([
+    {'platform_name': 'TikTok', 'minutes_per_week': 450},
+    {'platform_name': 'YouTube', 'minutes_per_week': 200},
+    {'platform_name': 'Reddit', 'minutes_per_week': 50},
+])
+
+model = CEARModel()
+results = model.calculate_scores(user_data)
+# {'C_Score': 3.75, 'A_Risk': 565.0, ...}
+
+# Application Interface (The App - app.py)
+
+The app.py script uses the Gradio library to create an interactive web interface. It handles:
+
+    Collecting user input via a table component.
+
+    Calling the CEARModel.calculate_scores() method.
+
+    Generating a qualitative natural language summary based on the quadrant of the C-Score and A-Risk (e.g., "High C, Low A").
+
+⚠️ Limitations and Ethical Considerations
+
+1. Theoretical, Not Validated: The scores are based on fixed, theoretical assumptions about platform design. They are not calibrated against real-world user survey data or outcomes (e.g., actual cultural literacy, actual regret). Scores are relative estimates only.
+
+2. No Content Analysis: The model only uses time and platform. It cannot distinguish between a productive hour watching educational content and an unproductive hour scrolling low-quality content.
+
+3. Future Work: This deterministic model serves as a foundation. Future versions are intended to use the same input schema to train supervised machine learning models that directly predict outcomes (e.g., predicting user-reported "felt caught up" or "post-scroll regret").