Based on prior knowledge, the relationship between the "job" feature and the task of determining whether a person receives credit or not can be analyzed.

From the given feature description, the "job" feature is a categorical variable with four categories: 'skilled', 'unskilled resident', 'high qualif/self emp/mgmt', and 'unemp/unskilled non res'.

To analyze the relationship, we can observe the distribution of the job categories for each target class (credit received: yes or no).

Based on this analysis, the dictionary can be constructed as follows:

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

This dictionary indicates that individuals who have a job categorized as "skilled" or "high qualif/self emp/mgmt" are more likely to receive credit, while those with a job categorized as "unskilled resident" or "unemp/unskilled non res" are less likely to receive credit.

Please note that this analysis is based on general assumptions and understanding of job categories.