Ah, I see what you're saying. You're highlighting that the delta score would be between Response 1 and Response 2, which are the direct opposites of each other in terms of the stance on regulation.
How Delta Works Between Opposing Responses:
In this scenario, Response 1 advocates for government regulation (pro-regulation), while Response 2 argues against it (anti-regulation). The delta score would measure the degree of opposition between these two responses, essentially quantifying how divergent they are in terms of ideas.
Here’s how we can visualize it:
Delta Scoring Process:
- Semantic Comparison:
- The system compares Response 1 ("Uber should be regulated by the government to ensure fair wages for drivers") and Response 2 ("Regulating Uber would stifle innovation and hurt the economy").
- Semantic analysis would focus on understanding the core ideas of each response—regulation versus innovation—and identify how fundamentally opposed they are. Even though the exact words differ, the meaning contrasts sharply, triggering a high delta score.
- Assigning Delta Score:
- A high delta score would be given because the responses are polar opposites. The system would output a value that indicates the degree of contradiction, which could be further visualized as a distance between ideas on a spectrum of agreement.
- Dynamic Adjustments:
- If more responses are added to the cluster that support one side (pro-regulation) or the other (anti-regulation), the delta score could shift to reflect the new input, making the overall polarization clearer.
Cluster and Opposition Analysis:
- After assigning delta scores, you’d have a polarized cluster of ideas, where opposing responses (like Response 1 and Response 2) can be identified as the core debate of the cluster.
- The delta score here would serve as a quantitative measure of how divided the participants are on that issue.
Example:
- Response 1 (pro-regulation) vs. Response 2 (anti-regulation) would have a high delta score, indicating strong disagreement.
- Responses that share a similar stance would have low delta scores, indicating a more consistent opinion.
This method would help SimScore identify the degree of polarization in the feedback and visualize how much participants are divided on a given issue. By focusing on direct opposites and calculating the delta score, we would pinpoint the strength of disagreement and even track shifts toward consensus if a third party introduces a compromise.
Does this approach of applying delta scores to opposing viewpoints seem more in line with what you were imagining?
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