> For the complete documentation index, see [llms.txt](https://consumer-trust-initiative.gitbook.io/consumer-trust-initiative-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://consumer-trust-initiative.gitbook.io/consumer-trust-initiative-docs/a.2-full-scoring-pseudocode.md).

# A.2 Full Scoring Pseudocode

```
// # --- PILLAR-SPECIFIC SCORING FUNCTIONS ---
# These functions are defined on their respective documentation pages.
# We assume they exist in this context.

def calculate_trust_score(site: SiteProfile) -> float:
    # ... logic as defined in section 3.4 ...
    # returns a score between 0 and 10
    pass

def calculate_data_depth_score(site: SiteProfile) -> float:
    # ... logic as defined in section 4.4 ...
    # returns a score between 0 and 10
    pass

def calculate_ux_score(site: SiteProfile) -> float:
    # ... logic as defined in section 5.4 ...
    # returns a score between 0 and 10
    pass

def calculate_x_factor_score(site: SiteProfile) -> float:
    # ... logic as defined in section 6.3 ...
    # returns a score between 0 and 10
    pass

# --- FINAL AGGREGATION MODEL ---

class TDUXFramework:
    def __init__(self, weights):
        self.weights = weights

    def calculate_final_score(self, site: SiteProfile) -> float:
        """
        Calculates the final weighted TDUX score for a given review site.
        """
        trust_score = calculate_trust_score(site)
        data_depth_score = calculate_data_depth_score(site)
        ux_score = calculate_ux_score(site)
        x_factor_score = calculate_x_factor_score(site)
        
        # Apply the defined weightings for the final composite score
        final_score = (
            (trust_score * self.weights['trust']) +
            (data_depth_score * self.weights['data_depth']) +
            (ux_score * self.weights['ux']) +
            (x_factor_score * self.weights['x_factor'])
        )
                      
        return round(final_score, 2)

# --- Example Usage ---

# Define the framework with the official weightings
TDUX_WEIGHTS = {
    'trust': 0.35,
    'data_depth': 0.30,
    'ux': 0.25,
    'x_factor': 0.10
}

tdux_model = TDUXFramework(weights=TDUX_WEIGHTS)

# Instantiate a profile for a hypothetical site
# In a real-world scenario, these boolean/numeric values would be the output of our research.
casimo_profile = SiteProfile(
    name="Casimo.org",
    has_named_authors=True,
    has_public_methodology=True,
    has_detailed_about_page=True,
    displays_license_prominently=True,
    links_to_ukgc=True,
    lists_substantive_cons=True,
    # ... and so on for all other metrics.
)

# Calculate the final score
final_score = tdux_model.calculate_final_score(casimo_profile)

print(f"The final TDUX score for {casimo_profile.name} is: {final_score}")
```


---

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