That is a really nice suggestion. I will try to see how can I actually do that when I will have the time (I’ve just started working on another project).
For me, the tomatometer was quite problematic for two reasons:
- It was difficult to integrate it in the framework of my analysis (bad-average-good).
- It is vague.
When I researched for the article, I read that the tomatometer represents the percentage of professional critic reviews that are positive for a given film.
I found the term “positive” vague, so I researched more, without much success.
On the contrary, my opinion was reinforced by scenarios like these, where two identical ratings are interpreted as positive or negative:
Now, addressing another issue. I don’t know what you actually understand by “biased”, but to me it sounds a bit harsh for what I’ve done in the article. I’ve made two assumptions:
- Movie ratings should reflect movie quality.
- Most people experience most movies as being of an average quality.
For 2), the other two main options were:
- Most people experience most movies as being of a low/high quality.
These other two options seemed really unlikely for me. Plus, for the first one I could actually provide one argument: my own experience.
So the main idea is that I chose to an assumption over another. I wouldn’t call it biased.
This kind of thing happens a lot in science, and it doesn’t seem reasonable to call it a biased approach. For example:
- In cosmology, when you construct your theoretical model, you may need to pick between these two assumptions: the universe is infinite/finite. You cannot prove true or false neither, so you’ll have to pick one and build a model.
- In all sciences, it assumed that all phenomena have a cause — sometimes, you observe a phenomenon, and you start searching for its cause, assuming thus that the phenomenon has a cause. You have no proof that that particular phenomenon has a cause, you just assume it does, by induction. It could be that we live in a supernatural world where not all phenomena have a cause — this is the alternative assumption.
I wouldn’t call these approaches biased. For me, “biased” is always associated with something negative. And I tried to do something positive, constructive in this article.
However, if you enlarge the domain of the term’s reference, I guess you can call these approaches biased, as well as my analysis.