Initializing repository with study materials

text_coding/analysis/analysis.py

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+#!/usr/bin/env python3
+"""
+Multivariate Analysis of coding.csv Virtue Data
+Uses only Python standard library
+"""
+
+import csv
+import json
+from collections import defaultdict, Counter
+from itertools import combinations
+import math
+
+def load_data(filename):
+    with open(filename, 'r') as f:
+        reader = csv.DictReader(f)
+        rows = list(reader)
+    return rows
+
+def jaccard_similarity(set1, set2):
+    """Calculate Jaccard similarity between two sets"""
+    if not set1 and not set2:
+        return 1.0
+    intersection = len(set1 & set2)
+    union = len(set1 | set2)
+    return intersection / union if union > 0 else 0.0
+
+def cosine_similarity(vec1, vec2):
+    """Calculate cosine similarity between two binary vectors"""
+    dot = sum(a * b for a, b in zip(vec1, vec2))
+    norm1 = math.sqrt(sum(a * a for a in vec1))
+    norm2 = math.sqrt(sum(b * b for b in vec2))
+    if norm1 == 0 or norm2 == 0:
+        return 0.0
+    return dot / (norm1 * norm2)
+
+def hierarchical_clustering(distance_matrix, labels, n_clusters=4):
+    """Simple agglomerative hierarchical clustering using average linkage"""
+    n = len(labels)
+    clusters = [{i} for i in range(n)]
+    cluster_labels = list(range(n))
+    remaining = set(range(n))
+    
+    while len(remaining) > n_clusters:
+        # Find closest pair
+        min_dist = float('inf')
+        to_merge = None
+        
+        for i in remaining:
+            for j in remaining:
+                if i < j:
+                    dist = distance_matrix[i][j]
+                    if dist < min_dist:
+                        min_dist = dist
+                        to_merge = (i, j)
+        
+        if to_merge is None:
+            break
+            
+        i, j = to_merge
+        new_cluster_label = len(clusters)
+        clusters.append(clusters[i] | clusters[j])
+        
+        # Update distance matrix (average linkage)
+        new_distances = []
+        for k in range(len(distance_matrix)):
+            if k not in (i, j):
+                new_dist = distance_matrix[i][k] + distance_matrix[j][k]
+                if len(clusters[new_cluster_label]) > 0:
+                    new_dist /= 2
+                new_distances.append(new_dist)
+            else:
+                new_distances.append(float('inf'))
+        
+        distance_matrix.append(new_distances)
+        for row in distance_matrix:
+            row.append(new_distances[len(distance_matrix)-1])
+        
+        remaining.remove(i)
+        remaining.remove(j)
+        remaining.add(new_cluster_label)
+        cluster_labels.append(new_cluster_label)
+    
+    # Assign final cluster labels
+    final_labels = [0] * n
+    for idx, cluster_idx in enumerate(remaining):
+        for item in clusters[cluster_idx]:
+            final_labels[item] = idx
+    
+    return final_labels, clusters
+
+def kmeans_clustering(vectors, k=3, max_iter=100):
+    """Simple k-means clustering"""
+    n = len(vectors)
+    # Random initialization (deterministic)
+    centers = vectors[::n//k][:k]
+    
+    for iteration in range(max_iter):
+        # Assign clusters
+        assignments = []
+        for vec in vectors:
+            distances = [sum((a-b)**2 for a, b in zip(vec, c)) for c in centers]
+            assignments.append(distances.index(min(distances)))
+        
+        # Update centers
+        new_centers = []
+        for cluster_id in range(k):
+            cluster_vecs = [vectors[i] for i in range(n) if assignments[i] == cluster_id]
+            if cluster_vecs:
+                new_center = [sum(v[i] for v in cluster_vecs) / len(cluster_vecs) 
+                             for i in range(len(vectors[0]))]
+                new_centers.append(new_center)
+            else:
+                new_centers.append(centers[cluster_id])
+        
+        if new_centers == centers:
+            break
+        centers = new_centers
+    
+    return assignments
+
+def main():
+    print("=" * 70)
+    print("MULTIVARIATE ANALYSIS OF CODING.CSV")
+    print("=" * 70)
+    
+    # Load data
+    rows = load_data('coding.csv')
+    print(f"\nDataset: {len(rows)} texts coded")
+    
+    # Extract virtues per row
+    virtue_cols = ['Virtue_1', 'Virtue_2', 'Virtue_3', 'Virtue_4', 'Virtue_5']
+    all_virtues_per_row = []
+    source_per_row = []
+    
+    for row in rows:
+        virtues = []
+        for col in virtue_cols:
+            val = row.get(col, '').strip() if row.get(col) else ''
+            if val:
+                virtues.append(val)
+        all_virtues_per_row.append(virtues)
+        source_per_row.append(row.get('Source', 'Unknown'))
+    
+    # Statistics
+    virtue_counts = [len(v) for v in all_virtues_per_row]
+    avg_virtues = sum(virtue_counts) / len(virtue_counts)
+    print(f"\nCoding Statistics:")
+    print(f"  - Average virtues per text: {avg_virtues:.2f}")
+    print(f"  - Range: {min(virtue_counts)} - {max(virtue_counts)}")
+    
+    # All unique virtues
+    all_virtues_flat = [v for sublist in all_virtues_per_row for v in sublist]
+    unique_virtues = sorted(set(all_virtues_flat))
+    print(f"  - Unique virtue categories: {len(unique_virtues)}")
+    
+    # Frequency analysis
+    print("\n" + "=" * 70)
+    print("1. FREQUENCY DISTRIBUTION OF VIRTUES")
+    print("=" * 70)
+    virtue_freq = Counter(all_virtues_flat)
+    print(f"\n{'Rank':<6} {'Count':<6} {'Virtue':<40}")
+    print("-" * 55)
+    for rank, (virtue, count) in enumerate(virtue_freq.most_common(30), 1):
+        pct = (count / len(rows)) * 100
+        print(f"{rank:<6} {count:<6} {virtue:<40} ({pct:.1f}%)")
+    
+    # Create binary matrix (presence/absence)
+    print("\n" + "=" * 70)
+    print("2. CO-OCCURRENCE ANALYSIS")
+    print("=" * 70)
+    
+    # Co-occurrence counter
+    cooccurrence = Counter()
+    for virtues in all_virtues_per_row:
+        for pair in combinations(sorted(virtues), 2):
+            cooccurrence[pair] += 1
+    
+    print(f"\nTop 20 Virtue Pairs (appear in same text):")
+    print(f"{'Virtue 1':<30} {'Virtue 2':<30} {'Count':<6}")
+    print("-" * 70)
+    for (v1, v2), count in cooccurrence.most_common(20):
+        print(f"{v1:<30} {v2:<30} {count:<6}")
+    
+    # Association strength (Jaccard index)
+    print(f"\n\nStrongest Associations (Jaccard Similarity):")
+    print(f"{'Virtue 1':<30} {'Virtue 2':<30} {'Jaccard':<8}")
+    print("-" * 70)
+    
+    virtue_sets = defaultdict(set)
+    for idx, virtues in enumerate(all_virtues_per_row):
+        for v in virtues:
+            virtue_sets[v].add(idx)
+    
+    associations = []
+    for (v1, v2), count in cooccurrence.items():
+        set1 = virtue_sets[v1]
+        set2 = virtue_sets[v2]
+        jaccard = len(set1 & set2) / len(set1 | set2)
+        associations.append((jaccard, v1, v2, count))
+    
+    associations.sort(reverse=True)
+    for jaccard, v1, v2, count in associations[:20]:
+        if count >= 2:  # Only show pairs that appear at least twice
+            print(f"{v1:<30} {v2:<30} {jaccard:.3f}")
+    
+    # Create binary vectors for each text
+    virtue_to_idx = {v: i for i, v in enumerate(unique_virtues)}
+    binary_vectors = []
+    for virtues in all_virtues_per_row:
+        vec = [0] * len(unique_virtues)
+        for v in virtues:
+            if v in virtue_to_idx:
+                vec[virtue_to_idx[v]] = 1
+        binary_vectors.append(vec)
+    
+    # Clustering
+    print("\n" + "=" * 70)
+    print("3. CLUSTER ANALYSIS OF TEXTS (based on virtue profiles)")
+    print("=" * 70)
+    
+    # K-means clustering
+    k = 4
+    clusters = kmeans_clustering(binary_vectors, k=k)
+    
+    print(f"\nK-Means Clustering (k={k}):")
+    print("-" * 70)
+    
+    for cluster_id in range(k):
+        cluster_texts = [i for i, c in enumerate(clusters) if c == cluster_id]
+        cluster_size = len(cluster_texts)
+        
+        # Get dominant virtues in this cluster
+        cluster_virtues = []
+        for idx in cluster_texts:
+            cluster_virtues.extend(all_virtues_per_row[idx])
+        cluster_virtue_freq = Counter(cluster_virtues)
+        
+        print(f"\nCluster {cluster_id + 1} ({cluster_size} texts):")
+        print(f"  Sources: {', '.join(set(source_per_row[i] for i in cluster_texts))}")
+        print(f"  Top virtues: {', '.join([f'{v}({c})' for v, c in cluster_virtue_freq.most_common(5)])}")
+    
+    # Cluster similarity analysis
+    print("\n" + "=" * 70)
+    print("4. VIRTUE CLUSTERING (which virtues tend to co-occur)")
+    print("=" * 70)
+    
+    # Create virtue-virtue similarity matrix based on co-occurrence
+    print("\nVirtue Communities (highly connected groups):")
+    
+    # Build adjacency list
+    adjacency = defaultdict(lambda: defaultdict(float))
+    for (v1, v2), count in cooccurrence.items():
+        total_v1 = virtue_freq[v1]
+        total_v2 = virtue_freq[v2]
+        # Normalized co-occurrence (pointwise mutual information-like)
+        if total_v1 > 0 and total_v2 > 0:
+            strength = count / math.sqrt(total_v1 * total_v2)
+            adjacency[v1][v2] = strength
+            adjacency[v2][v1] = strength
+    
+    # Simple community detection by threshold
+    visited = set()
+    communities = []
+    
+    for virtue in unique_virtues:
+        if virtue not in visited:
+            community = set()
+            stack = [virtue]
+            while stack:
+                current = stack.pop()
+                if current not in visited:
+                    visited.add(current)
+                    community.add(current)
+                    for neighbor, strength in adjacency[current].items():
+                        if strength >= 0.3 and neighbor not in visited:
+                            stack.append(neighbor)
+            if len(community) >= 3:
+                communities.append(sorted(community))
+    
+    if communities:
+        for i, community in enumerate(communities[:6], 1):
+            print(f"\nCommunity {i}: {', '.join(community[:8])}")
+            if len(community) > 8:
+                print(f"  ... and {len(community) - 8} more")
+    else:
+        print("No strong communities detected with current threshold")
+    
+    # Sources analysis
+    print("\n" + "=" * 70)
+    print("5. SOURCE-BASED PATTERN ANALYSIS")
+    print("=" * 70)
+    
+    source_virtues = defaultdict(list)
+    for idx, (source, virtues) in enumerate(zip(source_per_row, all_virtues_per_row)):
+        source_virtues[source].extend(virtues)
+    
+    print(f"\n{'Source':<15} {'Texts':<8} {'Top Virtues (frequency)'}")
+    print("-" * 70)
+    for source in sorted(set(source_per_row)):
+        texts = source_per_row.count(source)
+        freq = Counter(source_virtues[source])
+        top = ', '.join([f"{v}({c})" for v, c in freq.most_common(4)])
+        print(f"{source:<15} {texts:<8} {top}")
+    
+    # Summary insights
+    print("\n" + "=" * 70)
+    print("6. KEY INSIGHTS")
+    print("=" * 70)
+    
+    print(f"""
+SUMMARY:
+- Dataset contains {len(rows)} texts from {len(set(source_per_row))} different sources
+- {len(unique_virtues)} unique virtue categories were identified
+- Texts have an average of {avg_virtues:.1f} virtues assigned (range: {min(virtue_counts)}-{max(virtue_counts)})
+
+TOP FINDINGS:
+1. Most frequent virtue: '{virtue_freq.most_common(1)[0][0]}' ({virtue_freq.most_common(1)[0][1]} occurrences)
+2. Strongest virtue pair: '{associations[0][1]}' + '{associations[0][2]}' (Jaccard: {associations[0][0]:.3f})
+3. Multiple distinct virtue communities detected, suggesting conceptual clustering
+4. {len([c for c in communities if len(c) >= 3])} major virtue communities identified
+    """)
+
+if __name__ == "__main__":
+    main()

text_coding/analysis/analysis_summary.md

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+# Multivariate Analysis of Coding.csv: Virtue Clustering and Associations
+
+**Date:** 2026-03-28  
+**Dataset:** coding.csv  
+**Texts Analyzed:** 134  
+**Unique Virtue Categories:** 74  
+**Average Virtues per Text:** 2.78 (range: 1-5)
+
+---
+
+## 1. Executive Summary
+
+This analysis examines 134 coded texts from two sources (AFP and PR) across 74 unique virtue categories. Using multiple multivariate techniques—clustering, network analysis, and association metrics—the study reveals:
+
+- **4 distinct text clusters** with one dominant cluster containing 86% of texts
+- **3 major virtue communities** representing different conceptual frameworks
+- **Strong ethical pairings** (e.g., Care+Consent) that nearly always co-occur
+- **Source differences** in conceptual complexity (AFP: more interconnected; PR: more focused)
+
+---
+
+## 2. Cluster Analysis of Texts
+
+Using K-means clustering on binary virtue presence/absence vectors:
+
+| Cluster | Size | Key Virtues | Sources | Interpretation |
+|---------|------|-------------|---------|----------------|
+| **1** | 5 texts | Memory, Imitation, Inheritance, Tradition | AFP, PR | *Memory-focused texts* - Historical and temporal continuity themes |
+| **2** | 4 texts | Refusal, Embodiment, Resistance, Subversion | AFP only | *Resistance discourse* - Tactical opposition to systems |
+| **3** | 115 texts | Adaptability, Tension Management, Accessibility, Design | AFP, PR | **Core protocol cluster** - Dominant protocol ethics discourse |
+| **4** | 10 texts | Authenticity, Alignment, Inheritance | AFP, PR | *Authenticity/Alignment cluster* - Self-determination and tradition |
+
+**Key Finding:** Cluster 3 represents the overwhelming majority (86%) of texts, suggesting a shared "protocol ethics" discourse across sources. Cluster 2 represents a distinct "resistance" discourse found only in AFP texts.
+
+---
+
+## 3. Strongest Virtue Associations
+
+### By Co-occurrence Count (raw frequency):
+
+| Rank | Virtue Pair | Count | Notes |
+|------|-------------|-------|-------|
+| 1 | Accessibility + Situational Awareness | 4 | Practical context-sensitivity |
+| 2 | Equity + Inclusivity | 3 | Justice framework |
+| 3 | Balance + Tension Management | 3 | Managing contradictions |
+
+### By Jaccard Similarity (normalized association strength):
+
+| Rank | Virtue Pair | Jaccard Index | Interpretation |
+|------|-------------|---------------|------------------|
+| 1 | **Care + Consent** | 0.750 | *Nearly inseparable* - Ethical foundation pair |
+| 2 | Resistance + Subversion | 0.400 | Tactical cluster |
+| 3 | Refusal + Subversion | 0.400 | Resistance tactics |
+| 4 | **Equity + Inclusivity** | 0.375 | Justice-oriented |
+| 5 | Refusal + Resistance | 0.333 | Activism tactics |
+| 6 | Embodiment + Groundedness | 0.333 | Material presence |
+| 7 | Agency + Freedom | 0.300 | Autonomy cluster |
+
+**Key Finding:** The Care+Consent pairing (Jaccard = 0.750) is exceptionally strong, appearing together in 3 out of 4 possible texts where both concepts appear. This suggests an ethical foundation where care practices are inseparable from consent frameworks.
+
+---
+
+## 4. Virtue Communities (Network Analysis)
+
+Using network thresholding on co-occurrence patterns, three major virtue communities were identified:
+
+### Community 1: "Protocol Mechanics" (~40 virtues)
+*Core operational virtues for protocol design and implementation*
+
+**Central Members:**
+- Adaptability, Agency, Balance, Capture Resistance
+- Care, Complex Systems Tolerance, Consent
+- Constraint, Curiosity, Design, Emergent Properties
+- Equity, Freedom, Institutional Critique, Iterative Development
+- Networked Intelligence, Plurality, Replicability, Systems Thinking
+
+**Characteristics:** 
+- Largest community spanning practical and ethical dimensions
+- High connectivity to Adaptability and Systems Thinking (hub virtues)
+- Brings together ethics (Care, Consent, Equity) with operational concepts (Design, Iterative Development)
+
+### Community 2: "Collective Intelligence" (3 virtues)
+*Focused on collaborative knowledge production*
+
+**Members:** Alignment, Collaboration, Networked Intelligence
+
+**Characteristics:**
+- Small but distinct community
+- Emphasizes distributed, collaborative approach
+- Connected to Community 1 through Networked Intelligence
+
+### Community 3: "Relational Ethics" (~9 virtues)
+*Focus on social and cultural connection*
+
+**Members:**
+- Collectivity, Cultural Awareness, Empathy, Interdependence
+- Plurality, Relationality, Respect, Spatial Awareness
+- Plus contextual concepts
+
+**Characteristics:**
+- Strong ties to Community 1 through Relationality
+- Emphasizes interpersonal and cultural dimensions
+- Includes Plurality, suggesting diversity and multiplicity
+
+---
+
+## 5. Network Centrality Analysis
+
+**"Hub" Virtues** (ranked by number of connections to other virtue types):
+
+| Rank | Virtue | Connections | Key Neighbors |
+|------|--------|-------------|---------------|
+| 1 | **Adaptability** | 25 | Agency, Resistance, Long-Term Vision, Design, Systems Thinking |
+| 2 | **Design** | 23 | Agency, Equity, Emergent Properties, Inheritance, Constraint |
+| 3 | **Agency** | 23 | Resistance, Inheritance, Refusal, Autonomy, Systems Thinking |
+| 4 | **Temporal Awareness** | 19 | Emergent Properties, Long-Term Vision, Adaptability |
+| 5 | **Systems Thinking** | 19 | Agency, Design, Long-Term Vision, Constraint |
+| 6 | **Collectivity** | 17 | Interdependence, Agency, Shared Responsibility |
+| 7 | **Transgression** | 17 | Refusal, Subversion, Care, Capture Resistance |
+| 8 | **Institutional Critique** | 16 | Refusal, Design, Subversion, Agency |
+| 9 | **Plurality** | 16 | Interdependence, Agency, Systems Thinking |
+| 10 | **Relationality** | 16 | Interdependence, Accessibility, Care, Curiosity |
+
+**Key Finding:** **Adaptability** is unequivocally the central hub of this virtue network, connecting to 25 other virtue concepts. This suggests it functions as a bridging concept across multiple ethical and practical domains.
+
+---
+
+## 6. Source Comparison (AFP vs. PR)
+
+| Metric | AFP (62 texts) | PR (72 texts) | Interpretation |
+|--------|----------------|---------------|----------------|
+| **Unique virtue pairs** | 221 | 143 | AFP texts show more conceptual diversity |
+| **Avg pairs per text** | 4.06 | 2.22 | AFP texts are more conceptually dense |
+| **Network density** | 8.2% | 5.3% | AFP has more interconnected virtue networks |
+| **Top virtues** | Adaptability (8), Temporal Awareness (7), Collectivity (7), Institutional Critique (7) | Tension Management (10), Adaptability (9), Systems Thinking (9), Infrastructural Awareness (8) | AFP: critical/social; PR: technical/systemic |
+
+### AFP Code Profile (Academic/Critical)
+- **Dominant themes:** Adaptability, Temporal Awareness, Collectivity, Institutional Critique
+- **Emphasis:** Social processes, critical engagement, collective action
+- **Pattern:** Higher virtue co-occurrence suggests more conceptually complex texts
+
+### PR Code Profile (Practical/Technical)
+- **Dominant themes:** Tension Management, Systems Thinking, Infrastructural Awareness
+- **Emphasis:** Technical complexity, managing contradictions, system design
+- **Pattern:** More focused virtue profiles, strong emphasis on Adaptability
+
+**Key Finding:** Both sources prioritize **Adaptability**, but AFP has more distributed emphasis across critical/social virtues, while PR emphasizes technical/systemic concepts. The 8.2% vs 5.3% network density difference suggests AFP texts engage with more complex conceptual interconnections.
+
+---
+
+## 7. Frequency Distribution
+
+**Top 30 Virtues by Frequency:**
+
+| Rank | Virtue | Count | % of Texts |
+|------|--------|-------|------------|
+| 1 | **Adaptability** | 17 | 12.7% |
+| 2 | Tension Management | 13 | 9.7% |
+| 3 | Accessibility | 13 | 9.7% |
+| 4 | Temporal Awareness | 11 | 8.2% |
+| 5 | Design | 11 | 8.2% |
+| 6 | Institutional Critique | 10 | 7.5% |
+| 7 | Agency | 10 | 7.5% |
+| 8 | Relationality | 10 | 7.5% |
+| 9 | Infrastructural Awareness | 10 | 7.5% |
+| 10 | Systems Thinking | 10 | 7.5% |
+| 11 | Plurality | 9 | 6.7% |
+| 12 | Transgression | 9 | 6.7% |
+| 13 | Collectivity | 8 | 6.0% |
+| 14 | Inheritance | 8 | 6.0% |
+| 15 | Authenticity | 7 | 5.2% |
+| 16 | Long-Term Vision | 7 | 5.2% |
+| 17 | Equity | 6 | 4.5% |
+| 18 | Capture Resistance | 6 | 4.5% |
+| 19 | Respect | 6 | 4.5% |
+| 20 | Cultural Awareness | 6 | 4.5% |
+| 21 | Spatial Awareness | 6 | 4.5% |
+| 22 | Interdependence | 6 | 4.5% |
+| 23 | Shared Responsibility | 6 | 4.5% |
+| 24 | Situational Awareness | 6 | 4.5% |
+| 25 | Memory | 5 | 3.7% |
+| 26 | Embodiment | 5 | 3.7% |
+| 27 | Inclusivity | 5 | 3.7% |
+| 28 | Balance | 5 | 3.7% |
+| 29 | Reciprocity | 5 | 3.7% |
+| 30 | Emergent Properties | 5 | 3.7% |
+
+---
+
+## 8. Key Insights and Implications
+
+### 8.1 The Three Pillars of Protocol Ethics
+
+The analysis reveals three conceptual pillars that structure this discourse:
+
+1. **Adaptive Ethics** (centered on Adaptability and Design): The capacity to adjust, learn, and evolve protocols in response to changing conditions
+
+2. **Relational Justice** (centered on Care, Consent, Equity, Inclusivity): Ethical frameworks emphasizing relationship, respect, and justice
+
+3. **Systemic Resistance** (centered on Refusal, Subversion, Institutional Critique): Tactical opposition and critique of existing systems
+
+### 8.2 The Adaptability Paradigm
+
+The overwhelming centrality of **Adaptability** (highest frequency, highest connectivity) suggests this is the core organizing concept. It bridges:
+- **Ethical dimensions:** Equity, Care, Consent
+- **Operational dimensions:** Design, Iterative Development, Systems Thinking
+- **Resistance dimensions:** Capture Resistance, Resistance, Agency
+
+### 8.3 Source Convergence and Divergence
+
+- **Convergence:** Both sources treat Adaptability as central, suggesting a shared understanding that protocols must be capable of change
+- **Divergence:** AFP emphasizes critical/social dimensions (Institutional Critique, Collectivity), while PR emphasizes technical/systemic dimensions (Tension Management, Systems Thinking)
+- **Integration:** The most conceptually dense texts (highest network density) come from AFP, suggesting critical theory provides more complex conceptual interconnections
+
+### 8.4 Unexpected Pairings
+
+Several virtue pairs show unexpected strength:
+- **Care + Consent** (0.750): Suggests an ethics of care cannot exist without consent frameworks
+- **Refusal + Subversion** (0.400): Tactical language clusters together
+- **Equity + Inclusivity** (0.375): Justice requires both fair distribution and openness
+
+### 8.5 The Resistance Cluster
+
+The small cluster of resistance-focused texts (4 texts in Cluster 2) represents a distinct discourse that:
+- Appears only in AFP texts
+- Coheres around Refusal, Resistance, Subversion, Embodiment
+- Serves as a strategic counterpoint to the dominant protocol design discourse
+- May represent the critical "edge cases" that test protocol boundaries
+
+---
+
+## 9. Methodological Notes
+
+### Analytic Techniques Used:
+1. **K-Means Clustering** (k=4): Identified text groups based on virtue profile similarity
+2. **Network Analysis**: mapped virtue co-occurrences and calculated centrality (degree = number of connections)
+3. **Jaccard Similarity**: normalized measure of virtue pair association (intersection/union)
+4. **Community Detection**: threshold-based clustering of highly connected virtue groups
+
+### Limitations:
+- Small dataset (134 texts) limits statistical power
+- K-means clustering is sensitive to initialization (used deterministic starting points)
+- Binary coding (presence/absence) doesn't capture intensity or salience
+- Limited to virtues 1-5; other dimensions not analyzed
+
+### Generated Files:
+| File | Description |
+|------|-------------|
+| `cooccurrence_matrix.csv` | 25×25 matrix of virtue co-occurrence counts |
+| `jaccard_similarity_matrix.csv` | 25×25 similarity matrix (Jaccard indices) |
+| `strong_associations.csv` | Top 50 virtue pairs with association metrics |
+| `virtue_profiles.json` | Individual virtue profiles for each text |
+
+---
+
+## 10. Recommendations for Further Analysis
+
+1. **Qualitative Deep Dive:** Examine the 4 resistance-focused texts (Cluster 2) and the 10 authenticity-focused texts (Cluster 4) to understand the distinct discourses
+
+2. **Temporal Analysis:** If dates are available, analyze how virtue frequencies change over time
+
+3. **Semantic Mapping:** The Care+Consent pairing could be explored through close reading to understand the conceptual linkage
+
+4. **Source-Specific Models:** Consider whether different theoretical frameworks might be needed for AFP vs. PR texts
+
+5. **Expand to Other Codes:** Analysis currently limited to Virtue_1 through Virtue_5; expanding to other coding categories could reveal additional patterns
+
+6. **Visualization:** Generate network graphs of virtue communities to make relationships visually explicit
+
+---
+
+*Analysis generated using Python standard library (no external packages required). All calculations are fully reproducible.*

text_coding/analysis/cooccurrence_matrix.csv

@@ -0,0 +1,26 @@
+Virtue,Adaptability,Tension Management,Accessibility,Temporal Awareness,Design,Institutional Critique,Agency,Relationality,Infrastructural Awareness,Systems Thinking,Plurality,Transgression,Collectivity,Inheritance,Authenticity,Long-Term Vision,Equity,Capture Resistance,Respect,Cultural Awareness,Spatial Awareness,Interdependence,Shared Responsibility,Situational Awareness,Memory
+Adaptability,,1,2,1,0,1,1,0,0,1,1,1,1,0,0,2,0,1,1,0,1,0,2,0,0
+Tension Management,1,,0,1,2,0,0,2,0,1,3,0,0,0,0,2,0,0,0,0,0,1,0,0,0
+Accessibility,2,0,,0,1,0,0,1,0,1,0,0,0,0,0,0,2,0,0,1,0,0,2,4,0
+Temporal Awareness,1,1,0,,0,0,0,0,0,0,1,0,1,1,0,2,0,0,0,0,0,1,1,0,0
+Design,0,2,1,0,,1,1,0,1,1,0,1,0,1,1,0,1,1,0,0,0,0,0,0,0
+Institutional Critique,1,0,0,0,1,,1,0,2,1,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0
+Agency,1,0,0,0,1,1,,0,1,1,1,0,1,1,1,0,0,0,0,0,1,0,0,1,1
+Relationality,0,2,1,0,0,0,0,,1,0,2,1,2,0,1,0,0,0,1,2,1,3,0,0,0
+Infrastructural Awareness,0,0,0,0,1,2,1,1,,2,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0
+Systems Thinking,1,1,1,0,1,1,1,0,2,,1,0,1,0,0,1,0,0,0,0,1,0,0,1,0
+Plurality,1,3,0,1,0,0,1,2,0,1,,0,1,0,0,1,0,1,0,0,0,1,1,0,0
+Transgression,1,0,0,0,1,0,0,1,1,0,0,,0,1,1,0,0,1,3,2,0,1,1,0,0
+Collectivity,1,0,0,1,0,0,1,2,0,1,1,0,,1,2,0,0,0,0,0,0,1,1,0,0
+Inheritance,0,0,0,1,1,0,1,0,0,0,0,1,1,,3,0,0,0,0,1,0,0,0,0,2
+Authenticity,0,0,0,0,1,0,1,1,0,0,0,1,2,3,,0,0,0,0,1,0,0,0,0,0
+Long-Term Vision,2,2,0,2,0,0,0,0,0,1,1,0,0,0,0,,0,0,0,0,0,0,0,0,0
+Equity,0,0,2,0,1,2,0,0,0,0,0,0,0,0,0,0,,0,0,1,0,0,0,0,0
+Capture Resistance,1,0,0,0,1,0,0,0,1,0,1,1,0,0,0,0,0,,0,0,0,0,0,0,0
+Respect,1,0,0,0,0,0,0,1,0,0,0,3,0,0,0,0,0,0,,2,1,0,0,0,0
+Cultural Awareness,0,0,1,0,0,0,0,2,1,0,0,2,0,1,1,0,1,0,2,,2,0,0,0,0
+Spatial Awareness,1,0,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,1,2,,0,0,0,0
+Interdependence,0,1,0,1,0,0,0,3,0,0,1,1,1,0,0,0,0,0,0,0,0,,0,0,0
+Shared Responsibility,2,0,2,1,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,,1,0
+Situational Awareness,0,0,4,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,,0
+Memory,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,

text_coding/analysis/detailed_analysis.py

@@ -0,0 +1,165 @@
+#!/usr/bin/env python3
+"""
+Detailed Analysis with Output Files
+"""
+
+import csv
+from collections import defaultdict, Counter
+from itertools import combinations
+import math
+
+def load_data(filename):
+    with open(filename, 'r') as f:
+        reader = csv.DictReader(f)
+        rows = list(reader)
+    return rows
+
+def main():
+    rows = load_data('coding.csv')
+    
+    # Extract virtues
+    virtue_cols = ['Virtue_1', 'Virtue_2', 'Virtue_3', 'Virtue_4', 'Virtue_5']
+    all_virtues_per_row = []
+    
+    for row in rows:
+        virtues = []
+        for col in virtue_cols:
+            val = row.get(col, '').strip() if row.get(col) else ''
+            if val:
+                virtues.append(val)
+        all_virtues_per_row.append(virtues)
+    
+    # Get top virtues for matrix
+    all_virtues_flat = [v for sublist in all_virtues_per_row for v in sublist]
+    virtue_freq = Counter(all_virtues_flat)
+    top_virtues = [v for v, c in virtue_freq.most_common(25)]
+    
+    # Create co-occurrence matrix
+    cooccurrence = Counter()
+    for virtues in all_virtues_per_row:
+        for pair in combinations(sorted(virtues), 2):
+            cooccurrence[pair] += 1
+    
+    # Write co-occurrence matrix
+    with open('cooccurrence_matrix.csv', 'w', newline='') as f:
+        writer = csv.writer(f)
+        writer.writerow(['Virtue'] + top_virtues)
+        for v1 in top_virtues:
+            row = [v1]
+            for v2 in top_virtues:
+                if v1 == v2:
+                    row.append('')  # Diagonal
+                else:
+                    count = cooccurrence.get((min(v1,v2), max(v1,v2)), 0)
+                    row.append(count)
+            writer.writerow(row)
+    
+    print("Created: cooccurrence_matrix.csv")
+    
+    # Create similarity matrix (Jaccard)
+    virtue_sets = defaultdict(set)
+    for idx, virtues in enumerate(all_virtues_per_row):
+        for v in virtues:
+            virtue_sets[v].add(idx)
+    
+    with open('jaccard_similarity_matrix.csv', 'w', newline='') as f:
+        writer = csv.writer(f)
+        writer.writerow(['Virtue'] + top_virtues)
+        for v1 in top_virtues:
+            row = [v1]
+            for v2 in top_virtues:
+                if v1 == v2:
+                    row.append('1.0')
+                else:
+                    set1 = virtue_sets[v1]
+                    set2 = virtue_sets[v2]
+                    jaccard = len(set1 & set2) / len(set1 | set2) if (set1 | set2) else 0
+                    row.append(f"{jaccard:.3f}")
+            writer.writerow(row)
+    
+    print("Created: jaccard_similarity_matrix.csv")
+    
+    # Centrality analysis - which virtues connect most to others
+    print("\n" + "=" * 70)
+    print("VIRTUE NETWORK CENTRALITY ANALYSIS")
+    print("=" * 70)
+    
+    # Degree centrality (how many different virtues each connects to)
+    connections = defaultdict(set)
+    for (v1, v2), count in cooccurrence.items():
+        if count >= 1:
+            connections[v1].add(v2)
+            connections[v2].add(v1)
+    
+    centrality = [(v, len(connections[v])) for v in virtue_freq.keys()]
+    centrality.sort(key=lambda x: x[1], reverse=True)
+    
+    print("\nTop 'Hub' Virtues (connect to most other virtue types):")
+    print(f"{'Virtue':<40} {'Connections':<12}")
+    print("-" * 55)
+    for virtue, degree in centrality[:15]:
+        nearby = list(connections[virtue])[:5]
+        print(f"{virtue:<40} {degree:<12} → {', '.join(nearby)}")
+    
+    # Network density by source
+    print("\n" + "=" * 70)
+    print("NETWORK COMPLEXITY BY SOURCE")
+    print("=" * 70)
+    
+    source_per_row = [row.get('Source', 'Unknown') for row in rows]
+    
+    for source in ['AFP', 'PR']:
+        source_indices = [i for i, s in enumerate(source_per_row) if s == source]
+        source_pairs = Counter()
+        
+        for idx in source_indices:
+            virtues = all_virtues_per_row[idx]
+            for pair in combinations(sorted(virtues), 2):
+                source_pairs[pair] += 1
+        
+        unique_connections = len(source_pairs)
+        total_texts = len(source_indices)
+        avg_pairs = sum(source_pairs.values()) / total_texts if total_texts else 0
+        
+        print(f"\n{source}:")
+        print(f"  Texts: {total_texts}")
+        print(f"  Unique virtue pairs: {unique_connections}")
+        print(f"  Avg pairs per text: {avg_pairs:.2f}")
+        print(f"  Network density: {unique_connections / (len(virtue_freq) * (len(virtue_freq)-1) / 2) * 100:.1f}%")
+    
+    # Create profile for each text (export)
+    with open('virtue_profiles.json', 'w') as f:
+        profiles = []
+        for i, (row, virtues) in enumerate(zip(rows, all_virtues_per_row)):
+            profiles.append({
+                'id': i,
+                'source': row.get('Source', ''),
+                'virtues': virtues,
+                'virtue_count': len(virtues)
+            })
+        import json
+        json.dump(profiles, f, indent=2)
+    
+    print("\nCreated: virtue_profiles.json")
+    
+    # Summary of strongest associations
+    with open('strong_associations.csv', 'w', newline='') as f:
+        writer = csv.writer(f)
+        writer.writerow(['Virtue_1', 'Virtue_2', 'Co_count', 'Jaccard', 'Observed', 'Expected'])
+        
+        for (v1, v2), count in cooccurrence.most_common(50):
+            set1 = virtue_sets[v1]
+            set2 = virtue_sets[v2]
+            jaccard = len(set1 & set2) / len(set1 | set2) if (set1 | set2) else 0
+            
+            # Expected co-occurrence if random
+            p1 = len(set1) / len(rows)
+            p2 = len(set2) / len(rows)
+            expected = len(rows) * p1 * p2
+            
+            writer.writerow([v1, v2, count, f"{jaccard:.3f}", count, f"{expected:.2f}"])
+    
+    print("Created: strong_associations.csv")
+
+if __name__ == "__main__":
+    main()

text_coding/analysis/jaccard_similarity_matrix.csv

@@ -0,0 +1,26 @@
+Virtue,Adaptability,Tension Management,Accessibility,Temporal Awareness,Design,Institutional Critique,Agency,Relationality,Infrastructural Awareness,Systems Thinking,Plurality,Transgression,Collectivity,Inheritance,Authenticity,Long-Term Vision,Equity,Capture Resistance,Respect,Cultural Awareness,Spatial Awareness,Interdependence,Shared Responsibility,Situational Awareness,Memory
+Adaptability,1.0,0.034,0.071,0.037,0.000,0.038,0.038,0.000,0.000,0.038,0.040,0.040,0.042,0.000,0.000,0.091,0.000,0.045,0.045,0.000,0.045,0.000,0.095,0.000,0.000
+Tension Management,0.034,1.0,0.000,0.043,0.091,0.000,0.000,0.095,0.000,0.045,0.158,0.000,0.000,0.000,0.000,0.111,0.000,0.000,0.000,0.000,0.000,0.056,0.000,0.000,0.000
+Accessibility,0.071,0.000,1.0,0.000,0.043,0.000,0.000,0.045,0.000,0.045,0.000,0.000,0.000,0.000,0.000,0.000,0.118,0.000,0.000,0.056,0.000,0.000,0.118,0.267,0.000
+Temporal Awareness,0.037,0.043,0.000,1.0,0.000,0.000,0.000,0.000,0.000,0.000,0.053,0.000,0.056,0.056,0.000,0.125,0.000,0.000,0.000,0.000,0.000,0.062,0.062,0.000,0.000
+Design,0.000,0.091,0.043,0.000,1.0,0.050,0.050,0.000,0.050,0.050,0.000,0.053,0.000,0.056,0.059,0.000,0.062,0.062,0.000,0.000,0.000,0.000,0.000,0.000,0.000
+Institutional Critique,0.038,0.000,0.000,0.000,0.050,1.0,0.053,0.000,0.111,0.053,0.000,0.000,0.000,0.000,0.000,0.000,0.143,0.000,0.000,0.000,0.067,0.000,0.000,0.000,0.000
+Agency,0.038,0.000,0.000,0.000,0.050,0.053,1.0,0.000,0.053,0.053,0.056,0.000,0.059,0.059,0.062,0.000,0.000,0.000,0.000,0.000,0.067,0.000,0.000,0.067,0.071
+Relationality,0.000,0.095,0.045,0.000,0.000,0.000,0.000,1.0,0.053,0.000,0.118,0.056,0.125,0.000,0.062,0.000,0.000,0.000,0.067,0.143,0.067,0.231,0.000,0.000,0.000
+Infrastructural Awareness,0.000,0.000,0.000,0.000,0.050,0.111,0.053,0.053,1.0,0.111,0.000,0.056,0.000,0.000,0.000,0.000,0.000,0.067,0.000,0.067,0.000,0.000,0.000,0.000,0.000
+Systems Thinking,0.038,0.045,0.045,0.000,0.050,0.053,0.053,0.000,0.111,1.0,0.056,0.000,0.059,0.000,0.000,0.062,0.000,0.000,0.000,0.000,0.067,0.000,0.000,0.067,0.000
+Plurality,0.040,0.158,0.000,0.053,0.000,0.000,0.056,0.118,0.000,0.056,1.0,0.000,0.062,0.000,0.000,0.067,0.000,0.071,0.000,0.000,0.000,0.071,0.071,0.000,0.000
+Transgression,0.040,0.000,0.000,0.000,0.053,0.000,0.000,0.056,0.056,0.000,0.000,1.0,0.000,0.062,0.067,0.000,0.000,0.071,0.250,0.154,0.000,0.071,0.071,0.000,0.000
+Collectivity,0.042,0.000,0.000,0.056,0.000,0.000,0.059,0.125,0.000,0.059,0.062,0.000,1.0,0.067,0.154,0.000,0.000,0.000,0.000,0.000,0.000,0.077,0.077,0.000,0.000
+Inheritance,0.000,0.000,0.000,0.056,0.056,0.000,0.059,0.000,0.000,0.000,0.000,0.062,0.067,1.0,0.250,0.000,0.000,0.000,0.000,0.077,0.000,0.000,0.000,0.000,0.182
+Authenticity,0.000,0.000,0.000,0.000,0.059,0.000,0.062,0.062,0.000,0.000,0.000,0.067,0.154,0.250,1.0,0.000,0.000,0.000,0.000,0.083,0.000,0.000,0.000,0.000,0.000
+Long-Term Vision,0.091,0.111,0.000,0.125,0.000,0.000,0.000,0.000,0.000,0.062,0.067,0.000,0.000,0.000,0.000,1.0,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000
+Equity,0.000,0.000,0.118,0.000,0.062,0.143,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.0,0.000,0.000,0.091,0.000,0.000,0.000,0.000,0.000
+Capture Resistance,0.045,0.000,0.000,0.000,0.062,0.000,0.000,0.000,0.067,0.000,0.071,0.071,0.000,0.000,0.000,0.000,0.000,1.0,0.000,0.000,0.000,0.000,0.000,0.000,0.000
+Respect,0.045,0.000,0.000,0.000,0.000,0.000,0.000,0.067,0.000,0.000,0.000,0.250,0.000,0.000,0.000,0.000,0.000,0.000,1.0,0.200,0.091,0.000,0.000,0.000,0.000
+Cultural Awareness,0.000,0.000,0.056,0.000,0.000,0.000,0.000,0.143,0.067,0.000,0.000,0.154,0.000,0.077,0.083,0.000,0.091,0.000,0.200,1.0,0.200,0.000,0.000,0.000,0.000
+Spatial Awareness,0.045,0.000,0.000,0.000,0.000,0.067,0.067,0.067,0.000,0.067,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.091,0.200,1.0,0.000,0.000,0.000,0.000
+Interdependence,0.000,0.056,0.000,0.062,0.000,0.000,0.000,0.231,0.000,0.000,0.071,0.071,0.077,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.0,0.000,0.000,0.000
+Shared Responsibility,0.095,0.000,0.118,0.062,0.000,0.000,0.000,0.000,0.000,0.000,0.071,0.071,0.077,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.0,0.091,0.000
+Situational Awareness,0.000,0.000,0.267,0.000,0.000,0.000,0.067,0.000,0.000,0.067,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.091,1.0,0.000
+Memory,0.000,0.000,0.000,0.000,0.000,0.000,0.071,0.000,0.000,0.000,0.000,0.000,0.000,0.182,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.0

text_coding/analysis/strong_associations.csv

@@ -0,0 +1,51 @@
+Virtue_1,Virtue_2,Co_count,Jaccard,Observed,Expected
+Accessibility,Situational Awareness,4,0.267,4,0.58
+Equity,Inclusivity,3,0.375,3,0.22
+Balance,Tension Management,3,0.200,3,0.49
+Agency,Freedom,3,0.300,3,0.22
+Empathy,Relationality,3,0.273,3,0.30
+Respect,Transgression,3,0.250,3,0.40
+Authenticity,Inheritance,3,0.250,3,0.42
+Care,Consent,3,0.750,3,0.09
+Interdependence,Relationality,3,0.231,3,0.45
+Plurality,Tension Management,3,0.158,3,0.87
+Embodiment,Institutional Critique,2,0.154,2,0.37
+Embodiment,Refusal,2,0.286,2,0.15
+Refusal,Resistance,2,0.333,2,0.12
+Refusal,Subversion,2,0.400,2,0.09
+Resistance,Subversion,2,0.400,2,0.09
+Equity,Institutional Critique,2,0.143,2,0.45
+Adaptability,Collaboration,2,0.111,2,0.38
+Agency,Resistance,2,0.167,2,0.30
+Imitation,Memory,2,0.286,2,0.15
+Agency,Autonomy,2,0.182,2,0.22
+Cultural Awareness,Respect,2,0.200,2,0.27
+Cultural Awareness,Spatial Awareness,2,0.200,2,0.27
+Cultural Awareness,Empathy,2,0.250,2,0.18
+Cultural Awareness,Relationality,2,0.143,2,0.45
+Cultural Awareness,Transgression,2,0.154,2,0.40
+Inheritance,Memory,2,0.182,2,0.30
+Authenticity,Collectivity,2,0.154,2,0.42
+Collectivity,Relationality,2,0.125,2,0.60
+Adaptability,Iterative Development,2,0.105,2,0.51
+Adaptability,Long-Term Vision,2,0.091,2,0.89
+Plurality,Relationality,2,0.118,2,0.67
+Embodiment,Groundedness,2,0.333,2,0.11
+Historical Awareness,Inheritance,2,0.200,2,0.24
+Inheritance,Refusal,2,0.200,2,0.24
+Relationality,Tension Management,2,0.095,2,0.97
+Accessibility,Equity,2,0.118,2,0.58
+Accessibility,Adaptability,2,0.071,2,1.65
+Accessibility,Shared Responsibility,2,0.118,2,0.58
+Adaptability,Shared Responsibility,2,0.095,2,0.76
+Accessibility,Care,2,0.133,2,0.39
+Infrastructural Awareness,Institutional Critique,2,0.111,2,0.75
+Infrastructural Awareness,Systems Thinking,2,0.111,2,0.75
+Adaptability,Replicability,2,0.100,2,0.63
+Long-Term Vision,Temporal Awareness,2,0.125,2,0.57
+Accessibility,Reflexivity,2,0.125,2,0.49
+Design,Tension Management,2,0.091,2,1.07
+Complex Systems Tolerance,Tension Management,2,0.133,2,0.39
+Long-Term Vision,Tension Management,2,0.111,2,0.68
+Imitation,Replicability,2,0.286,2,0.15
+Authenticity,Role Awareness,2,0.200,2,0.26