Legal Research Guide

C - Conclusion (state your thesis)
R - Rule (present the legal rule with authority)
E - Explanation (analyze how courts have interpreted the rule)
A - Application (apply the rule to your specific scenario)
C - Conclusion (restate and refine conclusion)

# Case citation
Marbury v. Madison, 5 U.S. (1 Cranch) 137 (1803).
Brown v. Board of Education, 347 U.S. 483, 495 (1954).

# Statute citation
42 U.S.C. Section 1983 (2018).
Cal. Civ. Code Section 1798.100 (West 2020).  # California statute

# Law review article
Jane Smith, The Future of AI Regulation, 120 Harv. L. Rev. 456 (2024).

# Book
Richard Posner, Economic Analysis of Law 25 (9th ed. 2014).

# Short form citations (after first full citation)
Brown, 347 U.S. at 495.
Smith, supra note 12, at 460.
Id. at 462.  # Same source as immediately preceding citation

# Case citation
Donoghue v Stevenson [1932] AC 562 (HL).
R v Brown [1994] 1 AC 212, 237 (HL).

# Statute citation
Human Rights Act 1998, s 3.
Data Protection Act 2018, s 170(1).

# Journal article
Jane Smith, 'The Future of AI Regulation' (2024) 120 Modern Law Review 456.

# Book
Richard Posner, Economic Analysis of Law (9th edn, Aspen 2014) 25.

import requests
import json

# Using the CourtListener API (free, open-source)
BASE_URL = "https://www.courtlistener.com/api/rest/v3"

def search_opinions(query, court="scotus", page_size=20):
    """Search case opinions via CourtListener API."""
    response = requests.get(
        f"{BASE_URL}/search/",
        params={
            "q": query,
            "type": "o",  # opinions
            "court": court,
            "page_size": page_size,
            "order_by": "score desc"
        },
        headers={"Authorization": "Token YOUR_API_TOKEN"}
    )
    results = response.json()
    for case in results.get("results", []):
        print(f"[{case.get('dateFiled', 'N/A')}] {case.get('caseName', 'N/A')}")
        print(f"  Court: {case.get('court', 'N/A')}")
        print(f"  Citation: {case.get('citation', ['N/A'])[0] if case.get('citation') else 'N/A'}")
        print(f"  URL: https://www.courtlistener.com{case.get('absolute_url', '')}")
    return results

# Search for AI-related Supreme Court cases
results = search_opinions("artificial intelligence", court="scotus")

import networkx as nx

def build_citation_network(seed_case_ids, depth=2):
    """Build a citation network starting from seed cases."""
    G = nx.DiGraph()
    visited = set()
    queue = [(cid, 0) for cid in seed_case_ids]

    while queue:
        case_id, level = queue.pop(0)
        if case_id in visited or level > depth:
            continue
        visited.add(case_id)

        # Get case metadata and citations
        resp = requests.get(f"{BASE_URL}/opinions/{case_id}/",
                           headers={"Authorization": "Token YOUR_API_TOKEN"})
        if resp.status_code != 200:
            continue

        case = resp.json()
        case_name = case.get("case_name", f"Case {case_id}")
        G.add_node(case_id, name=case_name, date=case.get("date_filed"))

        # Get citing opinions (who cites this case)
        for cited_id in case.get("opinions_cited", []):
            G.add_edge(case_id, cited_id)
            if level < depth:
                queue.append((cited_id, level + 1))

    return G

# Analyze: which cases are most cited (highest in-degree)?
# These are the most authoritative precedents

# Analyzing legislative text complexity
import re
from textstat import textstat

def analyze_statute(text):
    """Compute readability metrics for statutory text."""
    return {
        "flesch_reading_ease": textstat.flesch_reading_ease(text),
        "flesch_kincaid_grade": textstat.flesch_kincaid_grade(text),
        "gunning_fog": textstat.gunning_fog(text),
        "word_count": textstat.lexicon_count(text),
        "sentence_count": textstat.sentence_count(text),
        "avg_sentence_length": textstat.avg_sentence_length(text),
        "defined_terms": len(re.findall(r'"[A-Z][^"]*"', text)),
        "cross_references": len(re.findall(r'[Ss]ection \d+', text))
    }

# Example: Analyze a section of the GDPR
gdpr_article_5 = """
Personal data shall be processed lawfully, fairly and in a transparent
manner in relation to the data subject; collected for specified, explicit
and legitimate purposes and not further processed in a manner that is
incompatible with those purposes; adequate, relevant and limited to what
is necessary in relation to the purposes for which they are processed.
"""
print(analyze_statute(gdpr_article_5))