Based on the given description, it appears that the feature "job" represents the employment status or occupation of a person. To understand how this feature relates to the task of determining whether a person receives credit or not, we can analyze the potential relationship.

Analyzing the relationship between the "job" feature and the target of receiving credit, we can make some assumptions based on prior knowledge:

1. Skilled jobs (e.g., professionals, skilled workers) may have a positive impact on the likelihood of receiving credit.
2. Unskilled resident jobs (e.g., laborers, unskilled workers) might have a neutral or negative impact on the likelihood of receiving credit.
3. High-quality, self-employed, or managerial jobs are likely to have a positive impact on the likelihood of receiving credit.
4. Unemployed or unskilled non-resident individuals may have a negative impact on the likelihood of receiving credit.

Based on these assumptions, we can create a dictionary as follows:

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

Please note that the analysis is based on general assumptions and might not capture specific nuances or correlations present in the data. Further analysis and modeling should be conducted to validate these assumptions and uncover the true relationship between the "job" feature and the target variable.