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Culture Survey

culture_survey

Safety Culture Survey — organizational AI safety culture assessment tool.

Generate, administer, and score safety culture questionnaires to evaluate how well an organization's practices align with AI safety best practices. Produces maturity scores across 6 dimensions, gap analysis, recommendations, and HTML reports.

Usage (CLI)::

python -m replication culture-survey                       # run full survey with defaults
python -m replication culture-survey --profile startup     # use preset org profile
python -m replication culture-survey --dimensions governance transparency
python -m replication culture-survey --format json         # JSON output
python -m replication culture-survey --format html -o report.html
python -m replication culture-survey --benchmark           # compare against industry
python -m replication culture-survey --list-profiles       # list org presets
python -m replication culture-survey --gaps-only           # show only gaps/weaknesses

Programmatic::

from replication.culture_survey import CultureSurvey, PROFILES
survey = CultureSurvey()
result = survey.assess(profile="startup")
print(result.render())
print(result.to_json())
html = result.to_html()

Question dataclass

A single survey question.

Source code in src/replication/culture_survey.py 
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@dataclass
class Question:
    """A single survey question."""
    id: str
    dimension: str
    text: str
    weight: float = 1.0
    maturity_level: int = 1  # 1-5, which maturity level this targets

OrgProfile dataclass

Preset organizational profile with simulated survey responses.

Source code in src/replication/culture_survey.py 
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@dataclass
class OrgProfile:
    """Preset organizational profile with simulated survey responses."""
    name: str
    label: str
    description: str
    response_ranges: Dict[str, Tuple[float, float]]  # dimension → (min_score, max_score) out of 5

DimensionResult dataclass

Score for a single dimension.

Source code in src/replication/culture_survey.py 
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@dataclass
class DimensionResult:
    """Score for a single dimension."""
    dimension: str
    label: str
    score: float  # 0-5
    max_score: float  # 5.0
    maturity_level: int
    maturity_label: str
    questions_count: int
    gap_from_benchmark: float
    recommendations: List[str]

SurveyResult dataclass

Complete survey result.

Source code in src/replication/culture_survey.py 
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@dataclass
class SurveyResult:
    """Complete survey result."""
    profile_name: str
    profile_label: str
    dimensions: List[DimensionResult]
    overall_score: float
    overall_maturity: int
    overall_maturity_label: str
    total_questions: int
    strengths: List[str]
    weaknesses: List[str]
    priority_actions: List[str]

    def render(self) -> str:
        """Render as formatted text."""
        lines: List[str] = []
        lines.append("=" * 70)
        lines.append("  AI SAFETY CULTURE SURVEY RESULTS")
        lines.append("=" * 70)
        lines.append(f"  Organization Profile: {self.profile_label}")
        lines.append(f"  Questions Assessed:   {self.total_questions}")
        lines.append(f"  Overall Score:        {self.overall_score:.1f} / 5.0")
        lines.append(f"  Maturity Level:       {self.overall_maturity} - {self.overall_maturity_label}")
        lines.append("")

        # Dimension scores with bar chart
        lines.append("─" * 70)
        lines.append("  DIMENSION SCORES")
        lines.append("─" * 70)
        max_label = max(len(d.label) for d in self.dimensions)
        for d in sorted(self.dimensions, key=lambda x: x.score, reverse=True):
            bar_len = int(d.score / 5.0 * 30)
            bar = "█" * bar_len + "░" * (30 - bar_len)
            gap_str = f"{'▲' if d.gap_from_benchmark >= 0 else '▼'}{abs(d.gap_from_benchmark):.1f}"
            lines.append(f"  {d.label:<{max_label}}  {bar} {d.score:.1f}/5  L{d.maturity_level}  {gap_str}")
        lines.append("")

        # Strengths
        if self.strengths:
            lines.append("─" * 70)
            lines.append("  ✅ STRENGTHS")
            lines.append("─" * 70)
            for s in self.strengths:
                lines.append(f"  • {s}")
            lines.append("")

        # Weaknesses
        if self.weaknesses:
            lines.append("─" * 70)
            lines.append("  ⚠️  GAPS & WEAKNESSES")
            lines.append("─" * 70)
            for w in self.weaknesses:
                lines.append(f"  • {w}")
            lines.append("")

        # Priority actions
        if self.priority_actions:
            lines.append("─" * 70)
            lines.append("  🎯 PRIORITY ACTIONS")
            lines.append("─" * 70)
            for i, a in enumerate(self.priority_actions, 1):
                lines.append(f"  {i}. {a}")
            lines.append("")

        # Per-dimension recommendations
        lines.append("─" * 70)
        lines.append("  📋 DETAILED RECOMMENDATIONS")
        lines.append("─" * 70)
        for d in self.dimensions:
            if d.recommendations:
                lines.append(f"\n  [{d.label}] (Level {d.maturity_level} → {d.maturity_level + 1})")
                for r in d.recommendations:
                    lines.append(f"    → {r}")

        lines.append("")
        lines.append("=" * 70)
        return "\n".join(lines)

    def to_dict(self) -> Dict[str, Any]:
        """Convert to JSON-serializable dict."""
        return {
            "profile": self.profile_name,
            "profile_label": self.profile_label,
            "overall_score": round(self.overall_score, 2),
            "overall_maturity": self.overall_maturity,
            "overall_maturity_label": self.overall_maturity_label,
            "total_questions": self.total_questions,
            "dimensions": [
                {
                    "dimension": d.dimension,
                    "label": d.label,
                    "score": round(d.score, 2),
                    "maturity_level": d.maturity_level,
                    "maturity_label": d.maturity_label,
                    "gap_from_benchmark": round(d.gap_from_benchmark, 2),
                    "recommendations": d.recommendations,
                }
                for d in self.dimensions
            ],
            "strengths": self.strengths,
            "weaknesses": self.weaknesses,
            "priority_actions": self.priority_actions,
        }

    def to_json(self, indent: int = 2) -> str:
        """Return JSON string."""
        return json.dumps(self.to_dict(), indent=indent)

    def to_html(self) -> str:
        """Generate self-contained HTML report."""
        dims_json = json.dumps([
            {"label": d.label, "score": round(d.score, 2), "benchmark": INDUSTRY_BENCHMARKS.get(d.dimension, 3.0)}
            for d in self.dimensions
        ])
        recs_html = ""
        for d in self.dimensions:
            if d.recommendations:
                items = "".join(f"<li>{_html.escape(r)}</li>" for r in d.recommendations)
                recs_html += f'<div class="rec-section"><h3>{_html.escape(d.label)} <span class="level">Level {d.maturity_level}</span></h3><ul>{items}</ul></div>'

        strengths_html = "".join(f"<li>✅ {_html.escape(s)}</li>" for s in self.strengths)
        weaknesses_html = "".join(f"<li>⚠️ {_html.escape(w)}</li>" for w in self.weaknesses)
        actions_html = "".join(f"<li>{_html.escape(a)}</li>" for i, a in enumerate(self.priority_actions, 1))

        return f"""<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width,initial-scale=1">
<title>AI Safety Culture Survey — {self.profile_label}</title>
<style>
*{{margin:0;padding:0;box-sizing:border-box}}
body{{font-family:system-ui,-apple-system,sans-serif;background:#0f172a;color:#e2e8f0;padding:2rem}}
.container{{max-width:900px;margin:0 auto}}
h1{{font-size:1.8rem;margin-bottom:.5rem;color:#38bdf8}}
h2{{font-size:1.3rem;margin:1.5rem 0 .8rem;color:#94a3b8;border-bottom:1px solid #334155;padding-bottom:.3rem}}
h3{{font-size:1rem;color:#e2e8f0;margin-bottom:.4rem}}
.header{{text-align:center;margin-bottom:2rem}}
.score-big{{font-size:3rem;font-weight:700;color:#38bdf8}}
.maturity-badge{{display:inline-block;padding:.3rem .8rem;border-radius:6px;font-weight:600;margin-top:.5rem}}
.maturity-1{{background:#7f1d1d;color:#fca5a5}}
.maturity-2{{background:#78350f;color:#fcd34d}}
.maturity-3{{background:#1e3a5f;color:#93c5fd}}
.maturity-4{{background:#14532d;color:#86efac}}
.maturity-5{{background:#4c1d95;color:#c4b5fd}}
.dim-bar{{margin:.6rem 0;display:flex;align-items:center;gap:.8rem}}
.dim-label{{width:220px;font-size:.9rem;text-align:right}}
.bar-bg{{flex:1;height:24px;background:#1e293b;border-radius:4px;position:relative;overflow:hidden}}
.bar-fill{{height:100%;border-radius:4px;transition:width .5s}}
.bar-benchmark{{position:absolute;top:0;height:100%;width:2px;background:#f59e0b;z-index:2}}
.bar-score{{font-size:.85rem;width:60px;text-align:left}}
.legend{{display:flex;gap:1.5rem;justify-content:center;margin:.8rem 0;font-size:.8rem;color:#94a3b8}}
.legend-item::before{{content:'';display:inline-block;width:12px;height:12px;border-radius:2px;margin-right:4px;vertical-align:middle}}
.legend-score::before{{background:#38bdf8}}
.legend-bench::before{{background:#f59e0b}}
ul{{list-style:none;padding-left:1rem}}
li{{margin:.3rem 0;font-size:.9rem}}
.rec-section{{background:#1e293b;padding:1rem;border-radius:8px;margin:.6rem 0}}
.rec-section ul{{padding-left:.5rem}}
.level{{font-size:.8rem;color:#94a3b8;font-weight:400}}
.cols{{display:grid;grid-template-columns:1fr 1fr;gap:1rem}}
@media(max-width:600px){{.cols{{grid-template-columns:1fr}}.dim-label{{width:140px}}}}
canvas{{max-width:400px;margin:1rem auto;display:block}}
</style>
<script src="https://cdn.jsdelivr.net/npm/chart.js@4/dist/chart.umd.min.js"></script>
</head>
<body>
<div class="container">
<div class="header">
<h1>🛡️ AI Safety Culture Survey</h1>
<p style="color:#94a3b8">{self.profile_label}</p>
<div class="score-big">{self.overall_score:.1f}<span style="font-size:1.2rem;color:#64748b"> / 5.0</span></div>
<div class="maturity-badge maturity-{self.overall_maturity}">Level {self.overall_maturity}: {self.overall_maturity_label}</div>
</div>

<h2>Dimension Scores</h2>
<div class="legend">
<span class="legend-item legend-score">Your Score</span>
<span class="legend-item legend-bench">Industry Benchmark</span>
</div>
{''.join(f'''<div class="dim-bar">
<span class="dim-label">{d.label}</span>
<div class="bar-bg">
<div class="bar-fill" style="width:{d.score/5*100:.0f}%;background:{'#22c55e' if d.gap_from_benchmark>=0 else '#f87171' if d.gap_from_benchmark<-0.5 else '#fbbf24'}"></div>
<div class="bar-benchmark" style="left:{INDUSTRY_BENCHMARKS.get(d.dimension,3)/5*100:.0f}%"></div>
</div>
<span class="bar-score">{d.score:.1f} L{d.maturity_level}</span>
</div>''' for d in sorted(self.dimensions, key=lambda x: x.score, reverse=True))}

<canvas id="radar" width="400" height="400"></canvas>

<div class="cols">
<div>
<h2>✅ Strengths</h2>
<ul>{strengths_html if strengths_html else '<li style="color:#64748b">None identified</li>'}</ul>
</div>
<div>
<h2>⚠️ Gaps</h2>
<ul>{weaknesses_html if weaknesses_html else '<li style="color:#64748b">None identified</li>'}</ul>
</div>
</div>

<h2>🎯 Priority Actions</h2>
<ol style="list-style:decimal;padding-left:2rem">{actions_html}</ol>

<h2>📋 Recommendations by Dimension</h2>
{recs_html}
</div>

<script>
const dims = {dims_json};
new Chart(document.getElementById('radar'),{{
type:'radar',
data:{{
labels:dims.map(d=>d.label),
datasets:[
{{label:'Your Score',data:dims.map(d=>d.score),borderColor:'#38bdf8',backgroundColor:'rgba(56,189,248,0.15)',pointBackgroundColor:'#38bdf8'}},
{{label:'Industry Benchmark',data:dims.map(d=>d.benchmark),borderColor:'#f59e0b',backgroundColor:'rgba(245,158,11,0.08)',pointBackgroundColor:'#f59e0b',borderDash:[5,3]}}
]
}},
options:{{
scales:{{r:{{min:0,max:5,ticks:{{stepSize:1,color:'#64748b',backdropColor:'transparent'}},grid:{{color:'#334155'}},pointLabels:{{color:'#94a3b8',font:{{size:11}}}}}}}},
plugins:{{legend:{{labels:{{color:'#94a3b8'}}}}}}
}}
}});
</script>
</body></html>"""

render() -> str

Render as formatted text.

Source code in src/replication/culture_survey.py 
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def render(self) -> str:
    """Render as formatted text."""
    lines: List[str] = []
    lines.append("=" * 70)
    lines.append("  AI SAFETY CULTURE SURVEY RESULTS")
    lines.append("=" * 70)
    lines.append(f"  Organization Profile: {self.profile_label}")
    lines.append(f"  Questions Assessed:   {self.total_questions}")
    lines.append(f"  Overall Score:        {self.overall_score:.1f} / 5.0")
    lines.append(f"  Maturity Level:       {self.overall_maturity} - {self.overall_maturity_label}")
    lines.append("")

    # Dimension scores with bar chart
    lines.append("─" * 70)
    lines.append("  DIMENSION SCORES")
    lines.append("─" * 70)
    max_label = max(len(d.label) for d in self.dimensions)
    for d in sorted(self.dimensions, key=lambda x: x.score, reverse=True):
        bar_len = int(d.score / 5.0 * 30)
        bar = "█" * bar_len + "░" * (30 - bar_len)
        gap_str = f"{'▲' if d.gap_from_benchmark >= 0 else '▼'}{abs(d.gap_from_benchmark):.1f}"
        lines.append(f"  {d.label:<{max_label}}  {bar} {d.score:.1f}/5  L{d.maturity_level}  {gap_str}")
    lines.append("")

    # Strengths
    if self.strengths:
        lines.append("─" * 70)
        lines.append("  ✅ STRENGTHS")
        lines.append("─" * 70)
        for s in self.strengths:
            lines.append(f"  • {s}")
        lines.append("")

    # Weaknesses
    if self.weaknesses:
        lines.append("─" * 70)
        lines.append("  ⚠️  GAPS & WEAKNESSES")
        lines.append("─" * 70)
        for w in self.weaknesses:
            lines.append(f"  • {w}")
        lines.append("")

    # Priority actions
    if self.priority_actions:
        lines.append("─" * 70)
        lines.append("  🎯 PRIORITY ACTIONS")
        lines.append("─" * 70)
        for i, a in enumerate(self.priority_actions, 1):
            lines.append(f"  {i}. {a}")
        lines.append("")

    # Per-dimension recommendations
    lines.append("─" * 70)
    lines.append("  📋 DETAILED RECOMMENDATIONS")
    lines.append("─" * 70)
    for d in self.dimensions:
        if d.recommendations:
            lines.append(f"\n  [{d.label}] (Level {d.maturity_level} → {d.maturity_level + 1})")
            for r in d.recommendations:
                lines.append(f"    → {r}")

    lines.append("")
    lines.append("=" * 70)
    return "\n".join(lines)

to_dict() -> Dict[str, Any]

Convert to JSON-serializable dict.

Source code in src/replication/culture_survey.py 
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def to_dict(self) -> Dict[str, Any]:
    """Convert to JSON-serializable dict."""
    return {
        "profile": self.profile_name,
        "profile_label": self.profile_label,
        "overall_score": round(self.overall_score, 2),
        "overall_maturity": self.overall_maturity,
        "overall_maturity_label": self.overall_maturity_label,
        "total_questions": self.total_questions,
        "dimensions": [
            {
                "dimension": d.dimension,
                "label": d.label,
                "score": round(d.score, 2),
                "maturity_level": d.maturity_level,
                "maturity_label": d.maturity_label,
                "gap_from_benchmark": round(d.gap_from_benchmark, 2),
                "recommendations": d.recommendations,
            }
            for d in self.dimensions
        ],
        "strengths": self.strengths,
        "weaknesses": self.weaknesses,
        "priority_actions": self.priority_actions,
    }

to_json(indent: int = 2) -> str

Return JSON string.

Source code in src/replication/culture_survey.py 
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def to_json(self, indent: int = 2) -> str:
    """Return JSON string."""
    return json.dumps(self.to_dict(), indent=indent)

to_html() -> str

Generate self-contained HTML report.

Source code in src/replication/culture_survey.py 
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    def to_html(self) -> str:
        """Generate self-contained HTML report."""
        dims_json = json.dumps([
            {"label": d.label, "score": round(d.score, 2), "benchmark": INDUSTRY_BENCHMARKS.get(d.dimension, 3.0)}
            for d in self.dimensions
        ])
        recs_html = ""
        for d in self.dimensions:
            if d.recommendations:
                items = "".join(f"<li>{_html.escape(r)}</li>" for r in d.recommendations)
                recs_html += f'<div class="rec-section"><h3>{_html.escape(d.label)} <span class="level">Level {d.maturity_level}</span></h3><ul>{items}</ul></div>'

        strengths_html = "".join(f"<li>✅ {_html.escape(s)}</li>" for s in self.strengths)
        weaknesses_html = "".join(f"<li>⚠️ {_html.escape(w)}</li>" for w in self.weaknesses)
        actions_html = "".join(f"<li>{_html.escape(a)}</li>" for i, a in enumerate(self.priority_actions, 1))

        return f"""<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width,initial-scale=1">
<title>AI Safety Culture Survey — {self.profile_label}</title>
<style>
*{{margin:0;padding:0;box-sizing:border-box}}
body{{font-family:system-ui,-apple-system,sans-serif;background:#0f172a;color:#e2e8f0;padding:2rem}}
.container{{max-width:900px;margin:0 auto}}
h1{{font-size:1.8rem;margin-bottom:.5rem;color:#38bdf8}}
h2{{font-size:1.3rem;margin:1.5rem 0 .8rem;color:#94a3b8;border-bottom:1px solid #334155;padding-bottom:.3rem}}
h3{{font-size:1rem;color:#e2e8f0;margin-bottom:.4rem}}
.header{{text-align:center;margin-bottom:2rem}}
.score-big{{font-size:3rem;font-weight:700;color:#38bdf8}}
.maturity-badge{{display:inline-block;padding:.3rem .8rem;border-radius:6px;font-weight:600;margin-top:.5rem}}
.maturity-1{{background:#7f1d1d;color:#fca5a5}}
.maturity-2{{background:#78350f;color:#fcd34d}}
.maturity-3{{background:#1e3a5f;color:#93c5fd}}
.maturity-4{{background:#14532d;color:#86efac}}
.maturity-5{{background:#4c1d95;color:#c4b5fd}}
.dim-bar{{margin:.6rem 0;display:flex;align-items:center;gap:.8rem}}
.dim-label{{width:220px;font-size:.9rem;text-align:right}}
.bar-bg{{flex:1;height:24px;background:#1e293b;border-radius:4px;position:relative;overflow:hidden}}
.bar-fill{{height:100%;border-radius:4px;transition:width .5s}}
.bar-benchmark{{position:absolute;top:0;height:100%;width:2px;background:#f59e0b;z-index:2}}
.bar-score{{font-size:.85rem;width:60px;text-align:left}}
.legend{{display:flex;gap:1.5rem;justify-content:center;margin:.8rem 0;font-size:.8rem;color:#94a3b8}}
.legend-item::before{{content:'';display:inline-block;width:12px;height:12px;border-radius:2px;margin-right:4px;vertical-align:middle}}
.legend-score::before{{background:#38bdf8}}
.legend-bench::before{{background:#f59e0b}}
ul{{list-style:none;padding-left:1rem}}
li{{margin:.3rem 0;font-size:.9rem}}
.rec-section{{background:#1e293b;padding:1rem;border-radius:8px;margin:.6rem 0}}
.rec-section ul{{padding-left:.5rem}}
.level{{font-size:.8rem;color:#94a3b8;font-weight:400}}
.cols{{display:grid;grid-template-columns:1fr 1fr;gap:1rem}}
@media(max-width:600px){{.cols{{grid-template-columns:1fr}}.dim-label{{width:140px}}}}
canvas{{max-width:400px;margin:1rem auto;display:block}}
</style>
<script src="https://cdn.jsdelivr.net/npm/chart.js@4/dist/chart.umd.min.js"></script>
</head>
<body>
<div class="container">
<div class="header">
<h1>🛡️ AI Safety Culture Survey</h1>
<p style="color:#94a3b8">{self.profile_label}</p>
<div class="score-big">{self.overall_score:.1f}<span style="font-size:1.2rem;color:#64748b"> / 5.0</span></div>
<div class="maturity-badge maturity-{self.overall_maturity}">Level {self.overall_maturity}: {self.overall_maturity_label}</div>
</div>

<h2>Dimension Scores</h2>
<div class="legend">
<span class="legend-item legend-score">Your Score</span>
<span class="legend-item legend-bench">Industry Benchmark</span>
</div>
{''.join(f'''<div class="dim-bar">
<span class="dim-label">{d.label}</span>
<div class="bar-bg">
<div class="bar-fill" style="width:{d.score/5*100:.0f}%;background:{'#22c55e' if d.gap_from_benchmark>=0 else '#f87171' if d.gap_from_benchmark<-0.5 else '#fbbf24'}"></div>
<div class="bar-benchmark" style="left:{INDUSTRY_BENCHMARKS.get(d.dimension,3)/5*100:.0f}%"></div>
</div>
<span class="bar-score">{d.score:.1f} L{d.maturity_level}</span>
</div>''' for d in sorted(self.dimensions, key=lambda x: x.score, reverse=True))}

<canvas id="radar" width="400" height="400"></canvas>

<div class="cols">
<div>
<h2>✅ Strengths</h2>
<ul>{strengths_html if strengths_html else '<li style="color:#64748b">None identified</li>'}</ul>
</div>
<div>
<h2>⚠️ Gaps</h2>
<ul>{weaknesses_html if weaknesses_html else '<li style="color:#64748b">None identified</li>'}</ul>
</div>
</div>

<h2>🎯 Priority Actions</h2>
<ol style="list-style:decimal;padding-left:2rem">{actions_html}</ol>

<h2>📋 Recommendations by Dimension</h2>
{recs_html}
</div>

<script>
const dims = {dims_json};
new Chart(document.getElementById('radar'),{{
type:'radar',
data:{{
labels:dims.map(d=>d.label),
datasets:[
{{label:'Your Score',data:dims.map(d=>d.score),borderColor:'#38bdf8',backgroundColor:'rgba(56,189,248,0.15)',pointBackgroundColor:'#38bdf8'}},
{{label:'Industry Benchmark',data:dims.map(d=>d.benchmark),borderColor:'#f59e0b',backgroundColor:'rgba(245,158,11,0.08)',pointBackgroundColor:'#f59e0b',borderDash:[5,3]}}
]
}},
options:{{
scales:{{r:{{min:0,max:5,ticks:{{stepSize:1,color:'#64748b',backdropColor:'transparent'}},grid:{{color:'#334155'}},pointLabels:{{color:'#94a3b8',font:{{size:11}}}}}}}},
plugins:{{legend:{{labels:{{color:'#94a3b8'}}}}}}
}}
}});
</script>
</body></html>"""

CultureSurvey

Generate and score safety culture assessments.

Source code in src/replication/culture_survey.py 
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class CultureSurvey:
    """Generate and score safety culture assessments."""

    def __init__(self, seed: Optional[int] = None):
        self._rng = random.Random(seed)

    def assess(
        self,
        profile: str = "scaleup",
        dimensions: Optional[List[str]] = None,
    ) -> SurveyResult:
        """Run assessment using an org profile preset."""
        if profile not in PROFILES:
            raise ValueError(f"Unknown profile '{profile}'. Available: {list(PROFILES)}")

        org = PROFILES[profile]
        active_dims = dimensions or list(DIMENSIONS.keys())
        active_questions = [q for q in QUESTION_BANK if q.dimension in active_dims]

        # Simulate responses based on profile ranges
        dim_scores: Dict[str, List[Tuple[float, float]]] = {}
        for q in active_questions:
            lo, hi = org.response_ranges.get(q.dimension, (2.0, 3.0))
            # Add some noise per question
            score = self._rng.uniform(lo, hi)
            score = max(0.0, min(5.0, score))
            dim_scores.setdefault(q.dimension, []).append((score, q.weight))

        # Compute weighted dimension scores
        dim_results: List[DimensionResult] = []
        for dim_key in active_dims:
            pairs = dim_scores.get(dim_key, [])
            if not pairs:
                continue
            total_weight = sum(w for _, w in pairs)
            weighted_sum = sum(s * w for s, w in pairs)
            avg = weighted_sum / total_weight if total_weight else 0
            maturity = max(1, min(5, int(avg) + (1 if avg % 1 >= 0.5 else 0)))
            mat_label, _ = MATURITY_LEVELS[maturity]
            benchmark = INDUSTRY_BENCHMARKS.get(dim_key, 3.0)
            gap = avg - benchmark

            # Get recommendations for next level
            recs = RECOMMENDATIONS.get(dim_key, {}).get(maturity, [])

            dim_results.append(DimensionResult(
                dimension=dim_key,
                label=DIMENSIONS[dim_key],
                score=round(avg, 2),
                max_score=5.0,
                maturity_level=maturity,
                maturity_label=mat_label,
                questions_count=len(pairs),
                gap_from_benchmark=round(gap, 2),
                recommendations=recs,
            ))

        # Overall
        if dim_results:
            overall = sum(d.score for d in dim_results) / len(dim_results)
        else:
            overall = 0.0
        overall_mat = max(1, min(5, round(overall)))
        overall_mat_label, _ = MATURITY_LEVELS[overall_mat]

        # Identify strengths and weaknesses
        sorted_dims = sorted(dim_results, key=lambda d: d.score, reverse=True)
        strengths = []
        weaknesses = []
        for d in sorted_dims:
            if d.gap_from_benchmark >= 0.3:
                strengths.append(f"{d.label}: {d.score:.1f}/5 (above benchmark by {d.gap_from_benchmark:.1f})")
            elif d.gap_from_benchmark <= -0.3:
                weaknesses.append(f"{d.label}: {d.score:.1f}/5 (below benchmark by {abs(d.gap_from_benchmark):.1f})")

        # Priority actions: top recommendations from weakest dimensions
        priority: List[str] = []
        for d in sorted(dim_results, key=lambda x: x.score):
            for r in d.recommendations[:2]:
                priority.append(f"[{d.label}] {r}")
                if len(priority) >= 5:
                    break
            if len(priority) >= 5:
                break

        return SurveyResult(
            profile_name=profile,
            profile_label=org.label,
            dimensions=dim_results,
            overall_score=round(overall, 2),
            overall_maturity=overall_mat,
            overall_maturity_label=overall_mat_label,
            total_questions=len(active_questions),
            strengths=strengths,
            weaknesses=weaknesses,
            priority_actions=priority,
        )

    def benchmark_compare(self, profile: str = "scaleup") -> str:
        """Compare a profile against all others and benchmarks."""
        lines = ["=" * 60, "  BENCHMARK COMPARISON", "=" * 60, ""]
        header = f"  {'Dimension':<30}"
        for name in PROFILES:
            header += f"  {name[:8]:>8}"
        header += "  {'Bench':>8}"
        lines.append(header)
        lines.append("  " + "─" * (30 + 10 * (len(PROFILES) + 1)))

        for dim_key, dim_label in DIMENSIONS.items():
            row = f"  {dim_label:<30}"
            for name, org in PROFILES.items():
                lo, hi = org.response_ranges.get(dim_key, (2.0, 3.0))
                mid = (lo + hi) / 2
                row += f"  {mid:>8.1f}"
            row += f"  {INDUSTRY_BENCHMARKS.get(dim_key, 3.0):>8.1f}"
            lines.append(row)

        lines.append("")
        return "\n".join(lines)

assess(profile: str = 'scaleup', dimensions: Optional[List[str]] = None) -> SurveyResult

Run assessment using an org profile preset.

Source code in src/replication/culture_survey.py 
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def assess(
    self,
    profile: str = "scaleup",
    dimensions: Optional[List[str]] = None,
) -> SurveyResult:
    """Run assessment using an org profile preset."""
    if profile not in PROFILES:
        raise ValueError(f"Unknown profile '{profile}'. Available: {list(PROFILES)}")

    org = PROFILES[profile]
    active_dims = dimensions or list(DIMENSIONS.keys())
    active_questions = [q for q in QUESTION_BANK if q.dimension in active_dims]

    # Simulate responses based on profile ranges
    dim_scores: Dict[str, List[Tuple[float, float]]] = {}
    for q in active_questions:
        lo, hi = org.response_ranges.get(q.dimension, (2.0, 3.0))
        # Add some noise per question
        score = self._rng.uniform(lo, hi)
        score = max(0.0, min(5.0, score))
        dim_scores.setdefault(q.dimension, []).append((score, q.weight))

    # Compute weighted dimension scores
    dim_results: List[DimensionResult] = []
    for dim_key in active_dims:
        pairs = dim_scores.get(dim_key, [])
        if not pairs:
            continue
        total_weight = sum(w for _, w in pairs)
        weighted_sum = sum(s * w for s, w in pairs)
        avg = weighted_sum / total_weight if total_weight else 0
        maturity = max(1, min(5, int(avg) + (1 if avg % 1 >= 0.5 else 0)))
        mat_label, _ = MATURITY_LEVELS[maturity]
        benchmark = INDUSTRY_BENCHMARKS.get(dim_key, 3.0)
        gap = avg - benchmark

        # Get recommendations for next level
        recs = RECOMMENDATIONS.get(dim_key, {}).get(maturity, [])

        dim_results.append(DimensionResult(
            dimension=dim_key,
            label=DIMENSIONS[dim_key],
            score=round(avg, 2),
            max_score=5.0,
            maturity_level=maturity,
            maturity_label=mat_label,
            questions_count=len(pairs),
            gap_from_benchmark=round(gap, 2),
            recommendations=recs,
        ))

    # Overall
    if dim_results:
        overall = sum(d.score for d in dim_results) / len(dim_results)
    else:
        overall = 0.0
    overall_mat = max(1, min(5, round(overall)))
    overall_mat_label, _ = MATURITY_LEVELS[overall_mat]

    # Identify strengths and weaknesses
    sorted_dims = sorted(dim_results, key=lambda d: d.score, reverse=True)
    strengths = []
    weaknesses = []
    for d in sorted_dims:
        if d.gap_from_benchmark >= 0.3:
            strengths.append(f"{d.label}: {d.score:.1f}/5 (above benchmark by {d.gap_from_benchmark:.1f})")
        elif d.gap_from_benchmark <= -0.3:
            weaknesses.append(f"{d.label}: {d.score:.1f}/5 (below benchmark by {abs(d.gap_from_benchmark):.1f})")

    # Priority actions: top recommendations from weakest dimensions
    priority: List[str] = []
    for d in sorted(dim_results, key=lambda x: x.score):
        for r in d.recommendations[:2]:
            priority.append(f"[{d.label}] {r}")
            if len(priority) >= 5:
                break
        if len(priority) >= 5:
            break

    return SurveyResult(
        profile_name=profile,
        profile_label=org.label,
        dimensions=dim_results,
        overall_score=round(overall, 2),
        overall_maturity=overall_mat,
        overall_maturity_label=overall_mat_label,
        total_questions=len(active_questions),
        strengths=strengths,
        weaknesses=weaknesses,
        priority_actions=priority,
    )

benchmark_compare(profile: str = 'scaleup') -> str

Compare a profile against all others and benchmarks.

Source code in src/replication/culture_survey.py 
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def benchmark_compare(self, profile: str = "scaleup") -> str:
    """Compare a profile against all others and benchmarks."""
    lines = ["=" * 60, "  BENCHMARK COMPARISON", "=" * 60, ""]
    header = f"  {'Dimension':<30}"
    for name in PROFILES:
        header += f"  {name[:8]:>8}"
    header += "  {'Bench':>8}"
    lines.append(header)
    lines.append("  " + "─" * (30 + 10 * (len(PROFILES) + 1)))

    for dim_key, dim_label in DIMENSIONS.items():
        row = f"  {dim_label:<30}"
        for name, org in PROFILES.items():
            lo, hi = org.response_ranges.get(dim_key, (2.0, 3.0))
            mid = (lo + hi) / 2
            row += f"  {mid:>8.1f}"
        row += f"  {INDUSTRY_BENCHMARKS.get(dim_key, 3.0):>8.1f}"
        lines.append(row)

    lines.append("")
    return "\n".join(lines)

main(argv: Optional[list] = None) -> None

CLI entry point.

Source code in src/replication/culture_survey.py 
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def main(argv: Optional[list] = None) -> None:
    """CLI entry point."""
    parser = argparse.ArgumentParser(
        prog="replication culture-survey",
        description="AI Safety Culture Survey — assess organizational safety maturity",
    )
    parser.add_argument(
        "--profile", "-p",
        choices=list(PROFILES.keys()),
        default="scaleup",
        help="Organization profile preset (default: scaleup)",
    )
    parser.add_argument(
        "--dimensions", "-d",
        nargs="+",
        choices=list(DIMENSIONS.keys()),
        help="Limit assessment to specific dimensions",
    )
    parser.add_argument(
        "--format", "-f",
        choices=["text", "json", "html"],
        default="text",
        help="Output format (default: text)",
    )
    parser.add_argument(
        "--output", "-o",
        help="Write output to file instead of stdout",
    )
    parser.add_argument(
        "--benchmark",
        action="store_true",
        help="Show benchmark comparison across all profiles",
    )
    parser.add_argument(
        "--list-profiles",
        action="store_true",
        help="List available organization profiles",
    )
    parser.add_argument(
        "--gaps-only",
        action="store_true",
        help="Show only gaps and weaknesses",
    )
    parser.add_argument(
        "--seed",
        type=int,
        default=None,
        help="Random seed for reproducible results",
    )

    args = parser.parse_args(argv)

    if args.list_profiles:
        print("Available organization profiles:\n")
        for name, prof in PROFILES.items():
            print(f"  {name:<15} {prof.label}")
            print(f"  {'':<15} {prof.description}\n")
        return

    survey = CultureSurvey(seed=args.seed)

    if args.benchmark:
        print(survey.benchmark_compare(args.profile))
        return

    result = survey.assess(
        profile=args.profile,
        dimensions=args.dimensions,
    )

    if args.gaps_only:
        if result.weaknesses:
            print("Gaps & Weaknesses:")
            for w in result.weaknesses:
                print(f"  ⚠️  {w}")
        if result.priority_actions:
            print("\nPriority Actions:")
            for i, a in enumerate(result.priority_actions, 1):
                print(f"  {i}. {a}")
        return

    if args.format == "json":
        output = result.to_json()
    elif args.format == "html":
        output = result.to_html()
    else:
        output = result.render()

    from ._helpers import emit_output
    emit_output(output, args.output)