Based on prior knowledge, we can analyze the relationship between the "job" feature and the target variable "Does this person receive a credit?".

From the given feature description, we can infer that the "job" feature represents the person's occupation. Different occupations may have varying levels of income stability and financial capability, which could influence their creditworthiness. It is reasonable to assume that the job type could impact the likelihood of someone receiving credit.

To generate the dictionary, we need to analyze the relationship between the "job" feature and the target variable.

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

```json
{
	"yes": ["skilled", "high qualif/self emp/mgmt"], 
	"no": ["unskilled resident", "unemp/unskilled non res"]
}
```

For the target class "yes" (the person receives credit), the given categories "skilled" and "high qualif/self emp/mgmt" are included, indicating that individuals with a skilled job or a job in higher qualification/self-employed/management roles are more likely to receive credit. 

For the target class "no" (the person does not receive credit), the categories "unskilled resident" and "unemp/unskilled non res" are included, suggesting that individuals with unskilled jobs or who are unemployed/unskilled non-residents are less likely to receive credit.

Note that other job categories might exist as well, but if their impact on the target variable is uncertain or hard to predict, they are not included in the dictionary.