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

Typically, a higher number of contacts during a campaign may indicate persistence or interest from the bank's side, which may increase the chances of a client subscribing to a term deposit. However, there is also a limit to how many contacts a client may be willing to receive, beyond which it might become annoying or intrusive, leading to a decrease in the likelihood of subscribing.

To generate the requested dictionary, we need to categorize the values of the "campaign" variable for both target classes, "yes" and "no". Here is an example of how the dictionary might look:

```json
{
	"no": [1.0, 2.0, 3.0, 4.0, 5.0],
	"yes": [1.0, 2.0, 3.0, 4.0, 5.0]
}
```

Please note that the example above only includes 5 typical values for each target class. In practice, the lists may contain more values depending on the specific dataset.