Based on prior knowledge, the workclass feature can provide insights into an individual's employment status, which can be correlated with their earnings. Here's an analysis of the relationship between workclass and the target variable:

- Private: Individuals working in the private sector may have both high and low incomes. It is difficult to predict if they earn more than $50,000 per year based on workclass alone.
- Local-gov: It is difficult to predict if individuals working for local government earn more than $50,000 per year based on workclass alone.
- ?: The question mark indicates missing values in the dataset. These missing values make it difficult to predict an individual's earnings solely based on workclass. We will exclude this category from the dictionary.
- Self-emp-not-inc: Self-employed individuals not incorporated may have variable incomes. It is difficult to predict if they earn more than $50,000 per year based on workclass alone.
- Federal-gov: It is difficult to predict if individuals working for the federal government earn more than $50,000 per year based on workclass alone.
- State-gov: It is difficult to predict if individuals working for the state government earn more than $50,000 per year based on workclass alone.
- Self-emp-inc: Self-employed individuals incorporated may have higher incomes, but it's not guaranteed that they earn more than $50,000 per year based on workclass alone.
- Without-pay: Individuals without pay are unlikely to earn more than $50,000 per year based on workclass alone.
- Never-worked: Individuals who have never worked are unlikely to earn more than $50,000 per year based on workclass alone.

Based on this analysis, the dictionary can be created as follows:

```json
{
	"no": ["Private", "Local-gov", "Self-emp-not-inc", "Federal-gov", "State-gov", "Without-pay", "Never-worked"],
	"yes": ["Self-emp-inc"]
}
```