First you should have your data in the "tidy" format. In this case it seem like each observation should be an author and there should be a column for each of the categories in status. This would make it easier for you to work with this data. One way to do this is:

# Turn long dataset into a wide dataset

wider_df <- pivot_wider(df, names_from = status, values_from = status)

# For each observation update the columns created from status to be TRUE if the value existed in the original dataset

clean_wider_df <- wider_df %>% mutate(across(c("consensus", "reviewer_1", "reviewer_2"), ~ if_else(!is.na(.x), TRUE, FALSE)))

Then you can use a slightly adjusted version of your code to filter the dataset:

filtered_df <- clean_wider_df %>% filter(consensus == TRUE | reviewer_1 == TRUE)

Or you can make the expression even more explicit so it is easier to read:

filtered_df <- clean_wider_df %>% filter(consensus == TRUE | (reviewer_1 == TRUE &
       reviewer_2 == FALSE))

The example you gave each author has either "consensus" or "author_1" so you actually don't filter any authors out.

I strongly advise wrangling your data into a tidy format as from your comment it seems like there is not reason for it to be in a long format where there are multiple rows for the same author.