| """Headline plot for exp0: the within-posts karma effect. |
| |
| Comparing AF posts only to AF posts, denser rationalist dialect still tracks |
| modestly higher karma. This isolates the real effect from the posts-vs-comments |
| confound that inflates the pooled correlation. |
| |
| Output: figures/headline_within_posts_karma.png |
| """ |
| import json |
| import os |
| import sys |
| from pathlib import Path |
| import numpy as np |
| import pandas as pd |
| import matplotlib.pyplot as plt |
| from scipy import stats |
|
|
| PROJECT_DIR = os.path.dirname(os.path.abspath(__file__)) |
| sys.path.insert(0, PROJECT_DIR) |
| from config import COMPILED, count_all_terms, word_count |
|
|
| DATA = Path(PROJECT_DIR) / "data" |
| FIG = Path(PROJECT_DIR) / "figures" |
| FIG.mkdir(parents=True, exist_ok=True) |
| START_YEAR = 2022 |
|
|
|
|
| def load_posts(): |
| rows = [] |
| with open(DATA / "alignmentforum_posts.jsonl") as f: |
| for line in f: |
| p = json.loads(line) |
| text = p.get("body") or "" |
| wc = max(1, word_count(text)) |
| counts = count_all_terms(text) |
| rate = sum((c / wc) * 10000 for c in counts.values()) |
| rows.append({"id": p["id"], "posted_at": p.get("posted_at"), |
| "base_score": p.get("base_score"), "dialect_rate": rate}) |
| df = pd.DataFrame(rows) |
| df["posted_at"] = pd.to_datetime(df["posted_at"], errors="coerce", utc=True) |
| df["year"] = df["posted_at"].dt.year |
| df["base_score"] = pd.to_numeric(df["base_score"], errors="coerce") |
| |
| df = df[(df["year"] >= START_YEAR) & (df["year"] <= 2025)] |
| df = df.dropna(subset=["base_score"]).reset_index(drop=True) |
| return df |
|
|
|
|
| def main(): |
| df = load_posts() |
| n = len(df) |
| rho, p = stats.spearmanr(df["dialect_rate"], df["base_score"]) |
| pear_r, pear_p = stats.pearsonr(df["dialect_rate"], df["base_score"]) |
| print(f"AF posts only, 2022-2025: n={n}") |
| print(f" Spearman rho={rho:+.3f} (p={p:.2g})") |
| print(f" Pearson r ={pear_r:+.3f} (p={pear_p:.2g})") |
|
|
| bins = [-0.001, 0, 5, 15, 30, 1e6] |
| labels = ["0", "(0, 5]", "(5, 15]", "(15, 30]", "> 30"] |
| df["bin"] = pd.cut(df["dialect_rate"], bins=bins, labels=labels) |
| g = df.groupby("bin", observed=True).agg( |
| mean_karma=("base_score", "mean"), |
| median_karma=("base_score", "median"), |
| std=("base_score", "std"), |
| n=("base_score", "count"), |
| ).reindex(labels) |
| ci = 1.96 * g["std"] / np.sqrt(g["n"].clip(lower=1)) |
|
|
| fig, ax = plt.subplots(figsize=(9, 5.5)) |
| x = np.arange(len(labels)) |
| ax.bar(x, g["mean_karma"], 0.62, yerr=ci, capsize=5, |
| color="tab:blue", alpha=0.88, label="mean karma (±95% CI)", |
| error_kw={"elinewidth": 1.4, "ecolor": "black"}) |
| ax.plot(x, g["median_karma"], "D--", color="tab:orange", markersize=8, |
| linewidth=1.8, label="median karma") |
| for i in x: |
| top = g["mean_karma"].iloc[i] + (ci.iloc[i] if not np.isnan(ci.iloc[i]) else 0) |
| ax.text(i, top + 2, f"n={int(g['n'].iloc[i])}", ha="center", |
| va="bottom", fontsize=9, color="dimgray") |
|
|
| ax.set_xticks(x) |
| ax.set_xticklabels(labels) |
| ax.set_xlabel("rationalist-dialect density (matches / 10k words, binned)", fontsize=11) |
| ax.set_ylabel("post karma (base_score)", fontsize=11) |
| ax.set_title("Within Alignment Forum posts, denser rationalist dialect\n" |
| "tracks modestly higher karma", fontsize=13, fontweight="bold") |
| ax.legend(fontsize=10, loc="upper left") |
| ax.grid(alpha=0.3, axis="y") |
| ax.margins(y=0.34) |
|
|
| ax.text(0.975, 0.965, |
| f"posts only, 2022–2025 n = {n:,}\n" |
| f"Spearman ρ = {rho:+.2f} (p = {p:.1g})", |
| transform=ax.transAxes, ha="right", va="top", fontsize=10, |
| bbox=dict(boxstyle="round,pad=0.45", facecolor="whitesmoke", |
| edgecolor="gray")) |
|
|
| fig.tight_layout() |
| out = FIG / "headline_within_posts_karma.png" |
| fig.savefig(out, dpi=140) |
| plt.close(fig) |
| print(f"wrote {out}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
