Based on my prior knowledge, the workclass feature could potentially be related to the earnings of an individual. Here's an analysis of the relationship between the workclass feature and the target variable (earning more than 50000 dollars per year):

- Private: It is possible that individuals working in the private sector may have lower salaries, but this is not a definitive indicator.
- Local-gov: Working in the local government sector may not necessarily guarantee higher salaries.
- ?: The missing values denoted by "?" provide no information about the employment status and thus cannot be used to make predictions.
- Self-emp-not-inc: Individuals who are self-employed but not incorporated may have varying income levels, making it difficult to make predictions.
- Federal-gov: Working in the federal government sector may offer job stability and competitive salaries, thus increasing the chances of earning more than 50000 dollars per year.
- State-gov: Similar to the local government sector, working in the state government sector may not guarantee higher salaries.
- Self-emp-inc: Individuals who are self-employed and incorporated may have higher chances of earning more than 50000 dollars per year due to the nature of their businesses.
- Without-pay: Individuals who are not receiving any pay are unlikely to earn more than 50000 dollars per year.
- Never-worked: Individuals who have never worked are unlikely to earn more than 50000 dollars per year.

Based on this analysis, I will create a dictionary with the possible values of the workclass feature for each target class:

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

Note that the list of values for the target class "no" includes all possible workclass values, including the uncertain "?" value, while the list for the target class "yes" only includes the workclass values that are more likely to result in earnings over 50000 dollars per year.