Based on prior knowledge, we can analyze the relationship between the "employment" feature and the task of determining whether a person receives credit or not.

By looking at the feature description, we can assume that the employment status of a person might have an impact on their creditworthiness. Generally, individuals who have been employed for a longer duration and have a stable source of income may have a higher chance of receiving credit.

The possible values for the "employment" feature are:

- '>=7': Present employment of at least 7 years
- '1<=X<4': Present employment between 1 to 4 years
- '4<=X<7': Present employment between 4 to 7 years
- 'unemployed': Currently unemployed
- '<1': Present employment less than 1 year

To create the dictionary with specific details, we need to determine the possible values of the "employment" feature for each target class (yes and no). Here is the resulting dictionary:

```json
{
	"yes": [">=7", "1<=X<4", "4<=X<7"],  
	"no": ["unemployed", "<1"]  
}
```

In this case, we considered the values '>=7', '1<=X<4', and '4<=X<7' for the "yes" target class, as these represent stable employment durations. For the "no" target class, we included the values 'unemployed' and '<1' since individuals who are unemployed or have very short employment duration may have a lower chance of receiving credit.

This analysis provides a general understanding of how the "employment" feature relates to the task of determining whether a person receives credit or not.