## **🔍 What Is Scholarly Scoring?**

Scholarly Scoring is a feature inside the **Scholar** tool (Left sidebar → Content → Scholar) that evaluates your content against trusted sources. It assigns scores based on two key dimensions: **content type classification** and **factuality evaluation**. Understanding how these work helps you interpret your scores accurately and make smarter optimization decisions.

## **🗂️ How Content Type Classification Works**

When Scholar analyzes a page, it determines the content type by reading signals from three layers of your page in the following priority order:

1. **Metadata** — The tool first reads your page's meta title, meta description, and structured data (schema markup). If your schema type is set to `Product` or your meta title includes commercial terms like "Buy" or "Shop," the tool will classify the page accordingly.
2. **Headings** — If metadata signals are ambiguous, Scholar scans your H1 and H2 tags for topic and intent cues. Headings with action-oriented or transactional language push the classification toward product or commercial pages.
3. **Body Content** — As a final signal, the tool analyzes the density and structure of your main body text, looking at keyword patterns, sentence structure, and topical focus.

This layered approach means the classification reflects the **strongest available signal**, not just the overall page topic.

## **⚠️ Why Blog Posts Sometimes Get Classified as Product Pages**

This is one of the most common classification issues. A blog post can be misclassified as a product page when any of the following are true:

- Your page uses **Product schema markup** even if the content is editorial.
- Your **meta title or meta description** includes commercial phrases such as "best," "buy," "price," "deals," or branded product names.
- Your **H1 or H2 headings** are written in a transactional style (e.g., "Top 10 [Product] to Buy in 2025").
- The body content contains a high density of **affiliate links, pricing mentions, or product comparisons**.

To fix a misclassification, audit your metadata and schema markup first — these carry the most weight. Update your schema type to **Article** or **BlogPosting** and ensure your meta title reflects an informational intent. After making changes, re-run the Scholar analysis to refresh the classification.

## **✅ How Factuality Scores Are Evaluated**

Factuality scoring measures how well the claims in your content are supported by credible, authoritative sources. Scholar cross-references your content against a curated index of scholarly and high-authority publications. The score is influenced by:

- **Claim density** — How many verifiable statements your content makes relative to its total length.
- **Source alignment** — Whether the facts in your content match or contradict data found in trusted references.
- **Citation presence** — Whether your content links to or references external authoritative sources directly.
- **Specificity** — Vague or generic statements score lower than precise, data-backed claims.

A higher factuality score signals to both Scholar and search engines that your content is trustworthy and well-researched.

## **📍 Factuality Scoring for Venue-Specific SEO Content**

Venue-specific content — such as pages optimized for a specific restaurant, hotel, event space, or local business — presents a unique challenge for factuality scoring. Because much of this content relies on **proprietary or hyper-local information** (e.g., seating capacity, operating hours, unique amenities), it may not appear in Scholar's reference index.

Here is how to maximize factuality scores for venue-specific pages:

1. **Add verifiable, public-record details** — Include information that exists in external sources, such as official business directories, Google Business Profile data, or local government records.
2. **Support unique claims with citations** — If you state a venue has won an award or holds a specific certification, link directly to the awarding body's page.
3. **Use structured data accurately** — Apply **LocalBusiness**, **FoodEstablishment**, or relevant schema types so Scholar can correctly interpret the content context before scoring.
4. **Avoid unsubstantiated superlatives** — Phrases like "the best venue in the city" without supporting evidence will lower your factuality score. Replace them with specific, provable claims.

Note that factuality scores for highly localized content will naturally be lower than scores for content covering well-documented topics. This is expected behavior, not a tool error.

## **🛠️ Quick Troubleshooting Checklist**

- Misclassified content type → Check and correct your schema markup first, then update meta tags.
- Low factuality score on a blog post → Add citations, increase claim specificity, and reduce unsubstantiated opinions.
- Low factuality score on a venue page → Link to public-record sources and apply the correct LocalBusiness schema.
- Score did not update after edits → Re-run the Scholar analysis manually to pull in the latest version of your page.

## **💬 Need More Help?**

If you need further assistance, open the chat widget in the bottom-right corner of the platform and type **human teammate** to be connected with a member of our team.

## **Additional Notes**

Weighted signal percentages (~45% metadata, ~35% structural/heading, ~20% body content) and the 70% confidence threshold rule, below which the tool falls back to closest match and may flag a low-confidence indicator.