After analyzing the relationship between the feature "job" and the task of whether the client subscribes to a term deposit, we can create a dictionary with specific details. 

Based on the analysis, the dictionary would be as follows:

```json
{
    "no": ["blue-collar", "unknown", "housemaid", "student", "unemployed"], 
    "yes": ["management", "technician", "entrepreneur", "retired", "admin.", "services", "self-employed"]
}
```

In this dictionary, the list of job values for the target class "no" includes 'blue-collar', 'unknown', 'housemaid', 'student', and 'unemployed'. These job categories indicate a lower likelihood of the client subscribing to a term deposit.

The list of job values for the target class "yes" includes 'management', 'technician', 'entrepreneur', 'retired', 'admin.', 'services', and 'self-employed'. These job categories indicate a higher likelihood of the client subscribing to a term deposit.

Please note that the "unknown" category is included in the "no" list as it represents job information that is not available.