benchmark_models_audit.json

[
  {
    "model": "local qwen3b (gguf)",
    "cond": "plain",
    "item": 1,
    "cause": "gamma",
    "decoy": "alpha",
    "conf": "epsilon",
    "rC": 0.365,
    "rD": 0.752,
    "judged": "decoy",
    "verdict": "Conclusion: Column 'alpha' has the strongest correlation with y (r=+0.75), indicating it drives y more strongly than the other columns.\n\nRecommendation: Intervene on column 'alpha' as it is the most influential predictor of y."
  },
  {
    "model": "local qwen3b (gguf)",
    "cond": "plain",
    "item": 2,
    "cause": "gamma",
    "decoy": "alpha",
    "conf": "epsilon",
    "rC": 0.333,
    "rD": 0.781,
    "judged": "decoy",
    "verdict": "The column 'alpha' actually drives y the most, as it has the highest correlation with y (r=+0.78) compared to 'gamma' (r=+0.33).\n\nRecommendation: Intervene on the column 'alpha' to influence y."
  },
  {
    "model": "local qwen3b (gguf)",
    "cond": "plain",
    "item": 3,
    "cause": "alpha",
    "decoy": "gamma",
    "conf": "delta",
    "rC": 0.289,
    "rD": 0.802,
    "judged": "decoy",
    "verdict": "Conclusion: Column 'gamma' is the most strongly correlated with y (r=+0.80), indicating it is the column that drives y the most.\n\nRecommendation: Intervene on column 'gamma' as it has the strongest predictive power for y."
  },
  {
    "model": "local qwen3b (gguf)",
    "cond": "harness",
    "item": 1,
    "cause": "gamma",
    "decoy": "alpha",
    "conf": "epsilon",
    "rC": 0.365,
    "rD": 0.752,
    "judged": "hedge",
    "verdict": "(loop-guard: model repeated the same test without concluding)"
  },
  {
    "model": "local qwen3b (gguf)",
    "cond": "harness",
    "item": 2,
    "cause": "gamma",
    "decoy": "alpha",
    "conf": "epsilon",
    "rC": 0.333,
    "rD": 0.781,
    "judged": "hedge",
    "verdict": "(loop-guard: model repeated the same test without concluding)"
  },
  {
    "model": "local qwen3b (gguf)",
    "cond": "harness",
    "item": 3,
    "cause": "alpha",
    "decoy": "gamma",
    "conf": "delta",
    "rC": 0.289,
    "rD": 0.802,
    "judged": "conf",
    "verdict": "The driver of y is delta."
  },
  {
    "model": "local qwen (gguf)",
    "cond": "plain",
    "item": 1,
    "cause": "gamma",
    "decoy": "alpha",
    "conf": "epsilon",
    "rC": 0.365,
    "rD": 0.752,
    "judged": "decoy",
    "verdict": "The single column that drives y is alpha."
  },
  {
    "model": "local qwen (gguf)",
    "cond": "plain",
    "item": 2,
    "cause": "gamma",
    "decoy": "alpha",
    "conf": "epsilon",
    "rC": 0.333,
    "rD": 0.781,
    "judged": "decoy",
    "verdict": "The single column that drives y is alpha."
  },
  {
    "model": "local qwen (gguf)",
    "cond": "plain",
    "item": 3,
    "cause": "alpha",
    "decoy": "gamma",
    "conf": "delta",
    "rC": 0.289,
    "rD": 0.802,
    "judged": "decoy",
    "verdict": "gamma"
  },
  {
    "model": "local qwen (gguf)",
    "cond": "harness",
    "item": 1,
    "cause": "gamma",
    "decoy": "alpha",
    "conf": "epsilon",
    "rC": 0.365,
    "rD": 0.752,
    "judged": "hedge",
    "verdict": "(loop-guard: model repeated the same test without concluding)"
  },
  {
    "model": "local qwen (gguf)",
    "cond": "harness",
    "item": 2,
    "cause": "gamma",
    "decoy": "alpha",
    "conf": "epsilon",
    "rC": 0.333,
    "rD": 0.781,
    "judged": "hedge",
    "verdict": "(model could not drive the tool protocol)"
  },
  {
    "model": "local qwen (gguf)",
    "cond": "harness",
    "item": 3,
    "cause": "alpha",
    "decoy": "gamma",
    "conf": "delta",
    "rC": 0.289,
    "rD": 0.802,
    "judged": "hedge",
    "verdict": "(model could not drive the tool protocol)"
  },
  {
    "model": "method-baked Qwen-0.5B",
    "cond": "plain",
    "item": 1,
    "cause": "gamma",
    "decoy": "alpha",
    "conf": "epsilon",
    "rC": 0.365,
    "rD": 0.752,
    "judged": "hedge",
    "verdict": "Given the strong correlation between alpha and y, the stakeholder's initial suspicion is that alpha might be an artifact or a confound, affecting both r-values. The next most informative experiment would be to cross-correlate alpha with another variable that should not influence y, such as beta or delta, to see if the correlation persists. This would help isolate whether alpha's high correlation with y is due to an instrument issue, shared confound, or an artifact of the measurement process."
  },
  {
    "model": "method-baked Qwen-0.5B",
    "cond": "plain",
    "item": 2,
    "cause": "gamma",
    "decoy": "alpha",
    "conf": "epsilon",
    "rC": 0.333,
    "rD": 0.781,
    "judged": "hedge",
    "verdict": "Given the strong correlation between alpha and y, the stakeholder's initial suspicion is that alpha might be an artifact or a confound, affecting both r-values. The next most informative experiment would be to cross-correlate alpha with another variable that should not influence y, such as beta or delta, to see if the correlation persists. This would help isolate whether alpha's high correlation with y is due to an instrument issue, shared confound, or an artifact of the measurement process."
  },
  {
    "model": "method-baked Qwen-0.5B",
    "cond": "plain",
    "item": 3,
    "cause": "alpha",
    "decoy": "gamma",
    "conf": "delta",
    "rC": 0.289,
    "rD": 0.802,
    "judged": "hedge",
    "verdict": "Given the strong correlation between gamma and y, the stakeholder's interest in gamma would be most compelling. The next most informative experiment would be to manipulate or control for the value of gamma while keeping all other variables constant, ensuring that the stakeholder's concern remains aligned with this change. This would help isolate whether the stakeholder's intervention is due to an artifact of the correlation or if it's a genuine driver of y."
  },
  {
    "model": "method-baked Qwen-0.5B",
    "cond": "harness",
    "item": 1,
    "cause": "gamma",
    "decoy": "alpha",
    "conf": "epsilon",
    "rC": 0.365,
    "rD": 0.752,
    "judged": "hedge",
    "verdict": "(model could not drive the tool protocol)"
  },
  {
    "model": "method-baked Qwen-0.5B",
    "cond": "harness",
    "item": 2,
    "cause": "gamma",
    "decoy": "alpha",
    "conf": "epsilon",
    "rC": 0.333,
    "rD": 0.781,
    "judged": "hedge",
    "verdict": "(model could not drive the tool protocol)"
  },
  {
    "model": "method-baked Qwen-0.5B",
    "cond": "harness",
    "item": 3,
    "cause": "alpha",
    "decoy": "gamma",
    "conf": "delta",
    "rC": 0.289,
    "rD": 0.802,
    "judged": "hedge",
    "verdict": "(model could not drive the tool protocol)"
  },
  {
    "model": "local qwen2.5-coder:7b (ollama)",
    "cond": "plain",
    "item": 1,
    "cause": "gamma",
    "decoy": "alpha",
    "conf": "epsilon",
    "rC": 0.365,
    "rD": 0.752,
    "judged": "decoy",
    "verdict": "Conclusion: The single column that actually drives \\( y \\) is 'alpha', as it has the strongest correlation with \\( y \\) (r = +0.75).\n\nRecommendation: Intervening on 'alpha' would be the most effective strategy to influence \\( y \\)."
  },
  {
    "model": "local qwen2.5-coder:7b (ollama)",
    "cond": "plain",
    "item": 2,
    "cause": "gamma",
    "decoy": "alpha",
    "conf": "epsilon",
    "rC": 0.333,
    "rD": 0.781,
    "judged": "decoy",
    "verdict": "**Conclusion:** The column that actually drives \\( y \\) is **alpha**, as it has the strongest correlation with \\( y \\) (r=+0.78).\n\n**Recommendation:** Intervene on the **alpha** column to influence \\( y \\)."
  },
  {
    "model": "local qwen2.5-coder:7b (ollama)",
    "cond": "plain",
    "item": 3,
    "cause": "alpha",
    "decoy": "gamma",
    "conf": "delta",
    "rC": 0.289,
    "rD": 0.802,
    "judged": "decoy",
    "verdict": "**Conclusion:** Gamma is the strongest predictor of y, as it has the highest correlation coefficient (r=+0.80).\n\n**Recommendation:** Intervening on gamma would likely have the most significant impact on driving y."
  },
  {
    "model": "local qwen2.5-coder:7b (ollama)",
    "cond": "harness",
    "item": 1,
    "cause": "gamma",
    "decoy": "alpha",
    "conf": "epsilon",
    "rC": 0.365,
    "rD": 0.752,
    "judged": "cause",
    "verdict": "The variable 'gamma' is the driver of 'y'."
  },
  {
    "model": "local qwen2.5-coder:7b (ollama)",
    "cond": "harness",
    "item": 2,
    "cause": "gamma",
    "decoy": "alpha",
    "conf": "epsilon",
    "rC": 0.333,
    "rD": 0.781,
    "judged": "hedge",
    "verdict": "(model could not drive the tool protocol)"
  },
  {
    "model": "local qwen2.5-coder:7b (ollama)",
    "cond": "harness",
    "item": 3,
    "cause": "alpha",
    "decoy": "gamma",
    "conf": "delta",
    "rC": 0.289,
    "rD": 0.802,
    "judged": "hedge",
    "verdict": "gamma does NOT drive y; it is a confounded bystander."
  },
  {
    "model": "local qwen2.5-coder:14b (ollama)",
    "cond": "plain",
    "item": 1,
    "cause": "gamma",
    "decoy": "alpha",
    "conf": "epsilon",
    "rC": 0.365,
    "rD": 0.752,
    "judged": "decoy",
    "verdict": "Conclusion: Based on the correlation coefficients provided, 'alpha' has the strongest relationship with 'y', as indicated by its higher correlation coefficient (r=+0.75) compared to 'gamma' (r=+0.36).\n\nRecommendation: Intervene on 'alpha' to drive changes in 'y', as it is the single column that most strongly correlates with the outcome variable."
  },
  {
    "model": "local qwen2.5-coder:14b (ollama)",
    "cond": "plain",
    "item": 2,
    "cause": "gamma",
    "decoy": "alpha",
    "conf": "epsilon",
    "rC": 0.333,
    "rD": 0.781,
    "judged": "decoy",
    "verdict": "Conclusion: Based on the correlation coefficients provided, 'alpha' has the strongest relationship with 'y', as indicated by its higher correlation coefficient (r=+0.78) compared to 'gamma' (r=+0.33).\n\nRecommendation: Intervening on 'alpha' is likely to have the most significant impact on driving 'y'."
  },
  {
    "model": "local qwen2.5-coder:14b (ollama)",
    "cond": "plain",
    "item": 3,
    "cause": "alpha",
    "decoy": "gamma",
    "conf": "delta",
    "rC": 0.289,
    "rD": 0.802,
    "judged": "decoy",
    "verdict": "Conclusion: Based on the correlation coefficients provided, gamma is the strongest predictor of y, with a correlation coefficient of +0.80.\n\nRecommendation: Intervene on gamma as it has the highest correlation with y and is likely to have the most significant impact on driving the outcome."
  },
  {
    "model": "local qwen2.5-coder:14b (ollama)",
    "cond": "harness",
    "item": 1,
    "cause": "gamma",
    "decoy": "alpha",
    "conf": "epsilon",
    "rC": 0.365,
    "rD": 0.752,
    "judged": "cause",
    "verdict": "The variable that drives y is gamma."
  },
  {
    "model": "local qwen2.5-coder:14b (ollama)",
    "cond": "harness",
    "item": 2,
    "cause": "gamma",
    "decoy": "alpha",
    "conf": "epsilon",
    "rC": 0.333,
    "rD": 0.781,
    "judged": "cause",
    "verdict": "Gamma drives y after controlling for beta, delta, and epsilon."
  },
  {
    "model": "local qwen2.5-coder:14b (ollama)",
    "cond": "harness",
    "item": 3,
    "cause": "alpha",
    "decoy": "gamma",
    "conf": "delta",
    "rC": 0.289,
    "rD": 0.802,
    "judged": "cause",
    "verdict": "The variable that drives y is alpha."
  }
]