After conducting a thorough analysis, it is difficult to determine how the feature "foreign_worker" directly relates to the task of determining whether a person receives a credit or not. The fact that a person is a foreign worker may not be a strong predictor of their creditworthiness, as it would heavily depend on other factors such as their income, employment history, credit history, etc.

However, based on the given feature and task, the dictionary can be created as follows:

```json
{
	"yes": ["yes"],
	"no": ["no"]
}
```

Since the feature "foreign_worker" is a categorical variable with only two possible values - "yes" and "no", we can simply include these two values in the dictionary for both target classes "yes" and "no". Although it is expected that the values "yes" and "no" would appear in the respective target classes, there could be other categories in the feature "foreign_worker" that are not known or do not have a clear relationship with the target variable. Therefore, it is not necessary to include these unknown or unclear categories in the dictionary, as the directive suggests not including them if they are hard to predict.

Please keep in mind that this is a general analysis based on the given feature and task. For a more accurate analysis and prediction, it would be essential to consider additional relevant features and apply appropriate machine learning techniques.