Based on prior knowledge, we can analyze the relationship between the "job" feature and the task of whether a client subscribes to a term deposit. 

First, let's take a look at how this feature may relate to the target variable:

1. "management" job: This category may be associated with higher income and job stability, which could increase the likelihood of subscribing to a term deposit.

2. "technician" job: This category may have a moderate income level and job stability, so it could be somewhat neutral in terms of subscribing to a term deposit.

3. "entrepreneur" job: Entrepreneurs usually have varying income levels and may take more risks, so the relationship with subscribing to a term deposit is uncertain.

4. "blue-collar" job: This category may have lower income and job stability compared to others, which might reduce the likelihood of subscribing to a term deposit.

5. "unknown" job: As the job information is unavailable, it is difficult to predict the relationship with subscribing to a term deposit.

6. "retired" job: Retired individuals may have stable income from retirement funds or pension, which could increase the likelihood of subscribing to a term deposit.

7. "admin." job: This category may have varying income levels and job stability, so the relationship with subscribing to a term deposit is uncertain.

8. "services" job: This category may have a moderate income level and job stability, so it could be somewhat neutral in terms of subscribing to a term deposit.

9. "self-employed" job: Self-employed individuals usually have varying income levels and may take more risks, so the relationship with subscribing to a term deposit is uncertain.

10. "unemployed" job: Unemployed individuals may have lower income and job stability, which might reduce the likelihood of subscribing to a term deposit.

11. "housemaid" job: This category may have lower income and job stability compared to others, which might reduce the likelihood of subscribing to a term deposit.

12. "student" job: Students typically have low income and may not be the primary target for subscribing to a term deposit.

Based on this analysis, we can create the following dictionary:

```json
{
	"no": ["blue-collar", "unemployed", "housemaid"],
	"yes": ["management", "retired"]
}
```
Please note that the values included in the dictionary are based on prior knowledge and assumptions, and the specific relationships may vary depending on the dataset and context.