diff --git a/repepo/experiments/plots/plot_ood_vs_id_persona_generalization_results.py b/repepo/experiments/plots/plot_ood_vs_id_persona_generalization_results.py
new file mode 100644
index 00000000..c79345cb
--- /dev/null
+++ b/repepo/experiments/plots/plot_ood_vs_id_persona_generalization_results.py
@@ -0,0 +1,100 @@
+import pandas as pd
+from repepo.experiments.persona_generalization import (
+    PersonaCrossSteeringExperimentResult,
+)
+from repepo.steering.evaluate_cross_steering import CrossSteeringResult
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+from statistics import mean
+from repepo.steering.steerability import (
+    get_steerability_slope,
+)
+from repepo.experiments.persona_prompts import CATEGORIZED_PERSONA_PROMPTS
+
+
+ds_to_category = {}
+for category, dataset_prompts in CATEGORIZED_PERSONA_PROMPTS.items():
+    for dataset in dataset_prompts.keys():
+        ds_to_category[dataset] = category
+
+
+def get_propensities(
+    cs: CrossSteeringResult, ds_index: int, sv_index: int, metric_name: str
+):
+    return [
+        *[
+            res[ds_index][sv_index].metrics[metric_name]
+            for res in cs.neg_steering.values()
+        ],
+        cs.dataset_baselines[ds_index].metrics[metric_name],
+        *[
+            res[ds_index][sv_index].metrics[metric_name]
+            for res in cs.pos_steering.values()
+        ],
+    ]
+
+
+def plot_ood_vs_id_persona_generalization_results(
+    results: dict[str, PersonaCrossSteeringExperimentResult],
+    title: str = "OOD vs ID steerability",
+    save_path: str | None = None,
+):
+    rows = []
+    for dataset, result in results.items():
+        cs = result.cross_steering_result
+        multipliers = [
+            *list(cs.neg_steering.keys()),
+            0.0,
+            *list(cs.pos_steering.keys()),
+        ]
+        baseline_sv_index = cs.steering_labels.index("baseline")
+        baseline_ds_index = cs.dataset_labels.index("baseline")
+        id_propensities_ld = get_propensities(
+            cs, baseline_ds_index, baseline_sv_index, "mean_logit_diff"
+        )
+        id_steerability = get_steerability_slope(
+            np.array(multipliers), np.array([id_propensities_ld])
+        )[0]
+        ood_steerabilities = []
+        for ds_index, ds_name in enumerate(cs.dataset_labels):
+            if ds_name == "baseline" or ds_name == "mean":
+                continue
+            propensities_ld = get_propensities(
+                cs, ds_index, baseline_sv_index, "mean_logit_diff"
+            )
+            steerability = get_steerability_slope(
+                np.array(multipliers), np.array([propensities_ld])
+            )[0]
+            ood_steerabilities.append(steerability)
+        ood_steerability = mean(ood_steerabilities)
+        rows.append(
+            {
+                "dataset": dataset,
+                "id_steerability": id_steerability,
+                "ood_steerability": ood_steerability,
+            }
+        )
+    df = pd.DataFrame.from_records(rows)
+
+    sns.set_theme(style="darkgrid")
+    ax = sns.scatterplot(
+        data=df,
+        x="id_steerability",
+        y="ood_steerability",
+    )
+    ax.set_title(title)
+    ax.set(
+        xlabel="In-distribution steerability", ylabel="Out-of-distribution steerability"
+    )
+
+    maxes = [df["id_steerability"].max(), df["ood_steerability"].max()]
+    mins = [df["id_steerability"].min(), df["ood_steerability"].min()]
+
+    ax.set_xlim(int(min(mins)) - 0.25, round(max(maxes)) + 0.25)
+    ax.set_ylim(int(min(mins)) - 0.25, round(max(maxes)) + 0.25)
+
+    if save_path is not None:
+        plt.savefig(save_path)
+
+    plt.show()