You seem to be thinking of all of these as things-that-can-be-implemented, which I don't think is exactly right. I think you could only call some of these "approaches to alignment", if you mean something that could some day be implemented and lead to aligned AI.
I'll consider two different properties:
Implementation: Theoretical (can't be implemented, takes infinite compute) or Implementable without ML (i.e. with humans, as a result it is very inefficient) or Implementation needs ML (there will still be humans, but the hope is that it will be efficient and competitive with unaligned AI systems).
How learning happens: Task-based (the agent learns to perform a particular task or reason in a particular way) or Reward-based (the agent learns to provide a good reward signal that provides good incentives for some other system). While task-based systems are more elegant and clean when everything works right, we'd expect that in the presence of real world messiness such as optimization difficulty that reward-based systems will be more robust (see Against Mimicry).
I would only consider the Implementation needs ML things to be "approaches to alignment". Anyway, here they are (ignoring ALBA for the same reason you do):
Weak HCH: A theoretical ideal where each human can delegate to other agents. We hope that the result is both superintelligent and aligned. Properties: Theoretical / Task-based
Strong HCH: Like weak HCH, but allowing each human to have a dialog with subagents, and allowing message passing to include pointers to other agents. Properties: Theoretical / Task-based
Meta-execution: A particular implementation method that can deal with the fact that some questions may be too "big" for any one agent to even read the full question. Properties: Not really a recursive approach, it's more a component of other approaches.
Factored cognition: The hypothesis that strong HCH can solve arbitrary tasks. In terms of actual implementation, it's compute-limited strong HCH / meta-execution. Properties: Implementable without ML / Task-based
Factored evaluation: The hypothesis that strong HCH can provide a reward signal for arbitrary tasks. (Less confident of this one, as it hasn't been explained in detail before.) In terms of actual implementation, it's compute-limited strong HCH / meta-execution, where the goal is to provide a reward signal for some task. Properties: Implementable without ML / Reward-based
Iterated amplification (IDA): Approximating strong/weak HCH by training an agent that behaves like depth-limited strong/weak HCH and increasing the effective depth over time. Properties: Implementation needs ML / Task-based
Recursive reward modeling: Approximating strong/weak HCH by training an agent that behaves like depth-limited strong/weak HCH on tasks that are useful for evaluating the task of interest, and on evaluating tasks that are useful for evaluating the task of interest, etc. Properties: Implementation needs ML / Reward-based
Debate: Not really a recursive method at all, but it still depends on the general premise of decomposing thought processes into trees of smaller thoughts (though in this case, the "smaller thoughts" have to be arguments and counterarguments, rather than general considerations). If the Factored cognition hypothesis is false, then debate is unlikely to work.