Based on my prior knowledge, the feature "capital-loss" refers to the financial loss incurred by an individual in the previous year. To analyze the relationship between this feature and the task of earning more than 50000 dollars per year, we can consider the following:

1. Individuals with a higher capital-loss may be less likely to earn more than 50000 dollars per year. This is because a significant financial loss in the previous year could indicate a decrease in income or financial instability.

2. The magnitude of the capital-loss may also play a role. Individuals with a higher capital-loss might have a lower probability of earning more than 50000 dollars per year compared to those with a lower capital-loss.

3. It is important to consider the range of values for the feature "capital-loss." By analyzing the data distribution, we can identify typical values that may be indicative of whether an individual earns more than 50000 dollars per year.

Based on these insights, let's create a dictionary to summarize the relationship between the feature "capital-loss" and the target variable:

```json
{
	"no": [0, 1500, 2000, 2500, 3000],
	"yes": [0, 1000, 1500, 2000, 2500]
}
```

In this dictionary, we have included typical values for the feature "capital-loss" for each target class. The values are presented as integers in lists. Please note that these values are examples, and the actual values may vary depending on the dataset being analyzed.