As a general policy, data should go first, conclusions second. I do not have much data on this topic, so I can’t say much specific about it.
I have a feeling there is some kind of “motte and bailey” about antifa, like on one hand it refers to some nebulous idea, on the other hand it refers to some specific people and organisations. So the “data” part should start with explaining who those people and organisations are, what is their role, whether they are respected by others who use the label and why (and how is this respect enforced in real life). Without this, you risk that whatever you say X about antifa, someone will reply “no, antifa is the general idea of being ‘against fascism’, it is unrelated to X”, which will be completely unproductive. You would probably reply “A, B, and C do X, here is evidence”, and the other person would go “you’re changing the topic, first you talked about antifa, now you talk about A, B, and C”. So, given that this seems quite predictable, you might start with extensional definition of antifa.
Definitions of “authoritarian right” and “non-antifa anti-authoritarian-right” would also be needed.
Followed by what specifically you mean by “impact on authoritarian right organizing”. Who did what, when? A few specific examples. Then, it becomes a relatively factual question to ask “why did X do Y?”. Then you could, dunno, keep four columns: “because of antifa”, “because of law enforcement”, “because of others”, “other causes”. Don’t forget the fourth one, things change for all kinds of reasons, maybe the authoritarian right has moved from MySpace to Facebook simply because that’s what everyone in their generation did; so you do not need to invent a separate reason for them. Then, look at the columns, and make your conclusion.
Not trying to debate politics here (frankly I don’t know anything about the three orgs you mentioned), just a general opinion on how this topic could be debated productively. Step one is to collect data, and maybe step zero is to make the kind of definitions that will let you know which data is relevant to your research and which is not.
As a general policy, data should go first, conclusions second. I do not have much data on this topic, so I can’t say much specific about it.
I have a feeling there is some kind of “motte and bailey” about antifa, like on one hand it refers to some nebulous idea, on the other hand it refers to some specific people and organisations. So the “data” part should start with explaining who those people and organisations are, what is their role, whether they are respected by others who use the label and why (and how is this respect enforced in real life). Without this, you risk that whatever you say X about antifa, someone will reply “no, antifa is the general idea of being ‘against fascism’, it is unrelated to X”, which will be completely unproductive. You would probably reply “A, B, and C do X, here is evidence”, and the other person would go “you’re changing the topic, first you talked about antifa, now you talk about A, B, and C”. So, given that this seems quite predictable, you might start with extensional definition of antifa.
Definitions of “authoritarian right” and “non-antifa anti-authoritarian-right” would also be needed.
Followed by what specifically you mean by “impact on authoritarian right organizing”. Who did what, when? A few specific examples. Then, it becomes a relatively factual question to ask “why did X do Y?”. Then you could, dunno, keep four columns: “because of antifa”, “because of law enforcement”, “because of others”, “other causes”. Don’t forget the fourth one, things change for all kinds of reasons, maybe the authoritarian right has moved from MySpace to Facebook simply because that’s what everyone in their generation did; so you do not need to invent a separate reason for them. Then, look at the columns, and make your conclusion.
Not trying to debate politics here (frankly I don’t know anything about the three orgs you mentioned), just a general opinion on how this topic could be debated productively. Step one is to collect data, and maybe step zero is to make the kind of definitions that will let you know which data is relevant to your research and which is not.