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A comprehensive skill for discovering, accessing, and organizing academic
philosophy literature. Supports scraping the Stanford Encyclopedia of Philosophy
(SEP) in compliance with its robots.txt, querying the PhilPapers API, systematic
literature review for philosophy, citation-genealogy tracing, and generating
Chicago-style (Notes and Author-Date) bibliographies.
Architecture Overview
Search Layer
├── search_sep() — SEP full-text search (HTML scraping)
├── search_philpapers() — PhilPapers REST API
└── search_crossref() — CrossRef DOI / citation metadata
Processing Layer
├── get_sep_article_bibliography() — extract SEP bibliography section
├── trace_concept_genealogy() — track concept citations over time
└── build_philosophy_bibliography() — merge, deduplicate, format
Output Layer
└── format_chicago_*() — Chicago Notes and Author-Date styles
"""
Search the Stanford Encyclopedia of Philosophy.
Uses SEP's built-in search endpoint. Results include entry title,
URL slug, and a short excerpt.
Parameters
----------
query : str
Search terms (natural language or Boolean: AND, OR, NOT).
category : str or None
Optional SEP category filter (e.g. 'ethics', 'metaphysics',
'logic-and-philosophy-of-logic').
max_results : int
Maximum number of results to return.
Returns
-------
list[dict]
Each dict: {'title', 'slug', 'url', 'excerpt', 'first_published',
'last_revised'}.
Notes
-----
SEP does not expose a public JSON API. This function scrapes the HTML
search results page. Abide by SEP's terms of use — no commercial use,
no bulk downloading of full article content.
"""
"""
Retrieve metadata for a single SEP entry (title, authors, dates, abstract).
Parameters
----------
entry_slug : str
The SEP entry slug (e.g. 'moral-luck', 'consciousness', 'free-will').
Returns
-------
dict
Keys: title, authors, first_published, last_revised, preamble,
section_titles, url.
"""
f"{_SEP_BASE}/entries/{entry_slug}/"
"h1"
True
if
else
""
# Authors appear in <div id="article-copyright">
"div"
id
"article-copyright"
True
if
else
""
# Publication info
"div"
id
"pubinfo"
True
if
else
""
# Preamble / abstract (first <p> after the TOC)
"div"
id
"preamble"
""
if
"p"
True
if
else
""
# Section titles
True
for
in
"div#main-text h2"
return
"title"
"authors"
"publication_info"
"preamble"
600
"section_titles"
"url"
"slug"
def
get_sep_article_bibliography
entry_slug: str
list
dict
"""
Extract the bibliography from a SEP article entry.
Parameters
----------
entry_slug : str
SEP entry slug (e.g. 'moral-luck').
Returns
-------
list[dict]
Each dict: {'raw_citation', 'authors', 'year', 'title_fragment'}.
"""
f"{_SEP_BASE}/entries/{entry_slug}/"
# Bibliography is in <div id="bibliography"> or <section id="bibliography">
"div"
id
"bibliography"
or
"section"
id
"bibliography"
if
not
return
for
in
"li"
" "
True
if
not
continue
# Attempt to extract year (4-digit number)
r"\b(1[5-9]\d{2}|20[0-2]\d)\b"
1
if
else
""
# Attempt to extract italic title (usually in <em>)
"em"
True
if
else
""
# Leading text up to year ~ author names
if
else
""
"raw_citation"
"authors"
120
"year"
"title_fragment"
120
return
2. PhilPapers API
_PHILPAPERS_API = "https://philpapers.org/api"defsearch_philpapers(
query: str,
categories: list[str] | None = None,
years: tuple[int, int] | None = None,
max_results: int = 30,
format_type: str = "json",
) -> list[dict]:
"""
Search PhilPapers — the largest philosophy bibliography database.
Parameters
----------
query : str
Search query string. Supports field search: author:Nagel, title:luck.
categories : list[str] or None
PhilPapers category codes (e.g. ['phil-mind', 'normative-ethics']).
See https://philpapers.org/categories.pl for codes.
years : tuple[int, int] or None
Inclusive year range filter: (start_year, end_year).
max_results : int
Maximum number of entries to return.
format_type : str
'json' (default) or 'bib' (BibTeX).
Returns
-------
list[dict]
Normalized entry dicts: title, authors, year, journal, volume,
pages, doi, abstract, philpapers_id, url.
"""
params: dict = {
"method": "search",
"query": query,
"limit": min(max_results, 100),
"format": format_type,
}
if categories:
params["categoryIds"] = ",".join(categories)
if years:
params["pubDateStart"] = years[0]
params["pubDateEnd"] = years[1]
resp = requests.get(_PHILPAPERS_API, params=params, timeout=30)
# PhilPapers API returns HTTP 200 even for empty results
resp.raise_for_status()
try:
data = resp.json()
except ValueError:
return []
entries = []
for item in (data ifisinstance(data, list) else data.get("entries", [])):
entry = {
"philpapers_id": item.get("id", ""),
"title": item.get("title", ""),
"authors": _parse_philpapers_authors(item.get("authors", [])),
"year": str(item.get("pubYear", "")),
"journal": item.get("journalTitle", "") or item.get("publication", ""),
"volume": str(item.get("volume", "")),
"pages": item.get("pages", ""),
"doi": item.get("doi", ""),
"abstract": (item.get("abstract") or"")[:400],
"url": f"https://philpapers.org/rec/{item.get('id', '')}",
}
entries.append(entry)
return entries
def_parse_philpapers_authors(authors_raw) -> str:
"""Convert PhilPapers author objects or strings to a comma-separated string."""ifnot authors_raw:
return""ifisinstance(authors_raw, str):
return authors_raw
names = []
for a in authors_raw:
ifisinstance(a, dict):
last = a.get("last", "")
first = a.get("first", "")
names.append(f"{last}, {first}".strip(", "))
elifisinstance(a, str):
names.append(a)
return"; ".join(names)
3. Bibliography Management and Citation Tracing
defbuild_philosophy_bibliography(
topic: str,
sources: list[str] | None = None,
years: tuple[int, int] | None = None,
max_per_source: int = 20,
) -> pd.DataFrame:
"""
Construct a unified bibliography by querying multiple philosophy sources.
Parameters
----------
topic : str
Research topic or concept (e.g. 'moral luck', 'consciousness').
sources : list[str] or None
Sources to query. Options: 'sep', 'philpapers'. Defaults to both.
years : tuple[int, int] or None
Year range filter for PhilPapers.
max_per_source : int
Maximum entries per source.
Returns
-------
pd.DataFrame
Deduplicated bibliography sorted by year descending.
Columns: title, authors, year, source, journal, doi, url, abstract.
"""if sources isNone:
sources = ["philpapers", "sep"]
all_records: list[dict] = []
if"philpapers"in sources:
pp_results = search_philpapers(topic, years=years, max_results=max_per_source)
for r in pp_results:
r["source"] = "PhilPapers"
all_records.extend(pp_results)
time.sleep(1.0)
if"sep"in sources:
sep_results = search_sep(topic, max_results=max_per_source)
for r in sep_results:
all_records.append({
"title": r["title"],
"authors": "",
"year": "",
"source": "SEP",
"journal": "Stanford Encyclopedia of Philosophy",
"doi": "",
"url": r["url"],
"abstract": r["excerpt"],
"philpapers_id": "",
"volume": "",
"pages": "",
})
ifnot all_records:
return pd.DataFrame()
df = pd.DataFrame(all_records)
# Deduplicate by normalised title
df["_title_norm"] = df["title"].str.lower().str.strip().str.replace(r"\s+", " ", regex=True)
df = df.drop_duplicates(subset="_title_norm").drop(columns=["_title_norm"])
df = df.sort_values("year", ascending=False).reset_index(drop=True)
return df
deftrace_concept_genealogy(
concept: str,
start_year: int = 1950,
end_year: int | None = None,
max_results: int = 100,
) -> pd.DataFrame:
"""
Trace the intellectual history of a philosophical concept over time.
Queries PhilPapers for works mentioning the concept, then groups and
counts publications by decade to reveal when the debate intensified.
Parameters
----------
concept : str
The philosophical concept to trace (e.g. 'moral luck', 'supervenience').
start_year : int
Earliest year to search.
end_year : int or None
Latest year to search. Defaults to current year.
max_results : int
Total maximum papers to retrieve.
Returns
-------
pd.DataFrame
Columns: title, authors, year, journal, doi, url, decade.
Sorted by year ascending.
"""if end_year isNone:
import datetime
end_year = datetime.date.today().year
results = search_philpapers(
concept,
years=(start_year, end_year),
max_results=max_results,
)
ifnot results:
return pd.DataFrame()
df = pd.DataFrame(results)
df["year_int"] = pd.to_numeric(df["year"], errors="coerce")
df = df.dropna(subset=["year_int"])
df["year_int"] = df["year_int"].astype(int)
df["decade"] = (df["year_int"] // 10) * 10
df = df.sort_values("year_int").reset_index(drop=True)
return df
defformat_chicago_notes(entry: dict) -> str:
"""
Format a bibliography entry in Chicago Notes style (footnote format).
Example output:
Thomas Nagel, "Moral Luck," *Mortal Questions* (Cambridge: Cambridge
University Press, 1979), 24–38.
Parameters
----------
entry : dict
Dict with keys: title, authors, year, journal, volume, pages, doi.
Returns
-------
str
Formatted Chicago Notes citation string.
"""
authors = entry.get("authors", "").replace(";", ",")
title = entry.get("title", "")
journal = entry.get("journal", "")
year = entry.get("year", "")
volume = entry.get("volume", "")
pages = entry.get("pages", "")
doi = entry.get("doi", "")
# Distinguish article vs bookif journal and journal != "Stanford Encyclopedia of Philosophy":
parts = [f'{authors}, "{title},"']
if journal:
parts.append(f"*{journal}*")
if volume:
parts.append(volume)
if year:
parts.append(f"({year})")
if pages:
parts.append(f": {pages}")
if doi:
parts.append(f". https://doi.org/{doi}")
return" ".join(parts).rstrip()
else:
# Book / encyclopedia entry
parts = [f'{authors}.']
if journal == "Stanford Encyclopedia of Philosophy":
parts.append(f'"{title}."')
parts.append("In *Stanford Encyclopedia of Philosophy*,")
parts.append(f"edited by Edward N. Zalta.")
if year:
parts.append(f"Last revised {year}.")
else:
parts.append(f"*{title}*.")
if year:
parts.append(f"{year}.")
return" ".join(parts)
defformat_chicago_author_date(entry: dict) -> str:
"""
Format a bibliography entry in Chicago Author-Date style.
Example output:
Nagel, Thomas. 1979. "Moral Luck." In *Mortal Questions*, 24–38.
Cambridge: Cambridge University Press.
Parameters
----------
entry : dict
Dict with keys: title, authors, year, journal, volume, pages, doi.
Returns
-------
str
Formatted Chicago Author-Date citation string.
"""
raw_authors = entry.get("authors", "")
title = entry.get("title", "")
journal = entry.get("journal", "")
year = entry.get("year", "n.d.")
volume = entry.get("volume", "")
pages = entry.get("pages", "")
doi = entry.get("doi", "")
# Reverse first author: "Last, First" for bibliography
first_author = raw_authors.split(";")[0].strip()
remaining = "; ".join(a.strip() for a in raw_authors.split(";")[1:])
author_part = first_author
if remaining:
author_part += f", and {remaining}"if journal and journal != "Stanford Encyclopedia of Philosophy":
vol_pages = f"{volume}" + (f": {pages}"if pages else"")
doi_part = f" https://doi.org/{doi}."if doi else""return (
f'{author_part}. {year}. "{title}." *{journal}* {vol_pages}.{doi_part}'
)
else:
returnf'{author_part}. {year}. "{title}." *Stanford Encyclopedia of Philosophy*.'
Example 1: Systematic Literature Review on "Moral Luck"
Perform a structured literature review on moral luck across SEP and PhilPapers,
build a unified bibliography, and analyse the concept's publication trajectory.
import pandas as pd
import json
TOPIC = "moral luck"
YEARS = (1970, 2025)
print(f"=== Systematic Philosophy Literature Review: {TOPIC!r} ===\n")
# ── Step 1: PhilPapers search ─────────────────────────────────────────────────print("Querying PhilPapers...")
pp_results = search_philpapers(
query=TOPIC,
years=YEARS,
max_results=50,
)
print(f" PhilPapers: {len(pp_results)} results")
# ── Step 2: SEP search ────────────────────────────────────────────────────────print("Querying SEP...")
sep_results = search_sep(TOPIC, max_results=10)
print(f" SEP: {len(sep_results)} entries")
# ── Step 3: Get SEP bibliography for the main entry ──────────────────────────print("\nFetching SEP bibliography for 'moral-luck' entry...")
sep_bib = get_sep_article_bibliography("moral-luck")
print(f" Found {len(sep_bib)} bibliography entries in SEP article.")
# ── Step 4: Unified bibliography ─────────────────────────────────────────────
bib_df = build_philosophy_bibliography(TOPIC, sources=["philpapers", "sep"], years=YEARS)
print(f"\nUnified bibliography: {len(bib_df)} entries")
# ── Step 5: Concept genealogy ─────────────────────────────────────────────────
genealogy = trace_concept_genealogy(TOPIC, start_year=1960)
ifnot genealogy.empty:
decade_counts = genealogy.groupby("decade").size().reset_index(name="publications")
print("\nPublications per decade:")
for _, row in decade_counts.iterrows():
bar = "#" * int(row["publications"])
print(f" {int(row['decade'])}s: {bar} ({int(row['publications'])})")
# ── Step 6: Format first 5 entries in Chicago Author-Date ────────────────────print("\n=== Sample Chicago Author-Date Citations ===")
for entry in pp_results[:5]:
citation = format_chicago_author_date(entry)
print(f" {citation}")
# ── Step 7: Save bibliography ─────────────────────────────────────────────────
bib_df.to_csv("moral_luck_bibliography.csv", index=False, encoding="utf-8")
# Export as BibTeX-like JSONL for Zotero importwithopen("moral_luck_bibliography.jsonl", "w", encoding="utf-8") as fh:
for _, row in bib_df.iterrows():
fh.write(json.dumps(row.to_dict(), ensure_ascii=False) + "\n")
print(f"\nSaved bibliography to moral_luck_bibliography.csv and .jsonl")
# ── Step 8: Show foundational papers ─────────────────────────────────────────print("\nEarliest papers (foundational texts):")
ifnot genealogy.empty:
earliest = genealogy.nsmallest(5, "year_int")[["title", "authors", "year", "journal"]]
print(earliest.to_string(index=False))
Example 2: Trace the Citation History of a Key Philosophical Paper
Identify works that cite or respond to a seminal paper by searching for its
author + key concepts, then map the intellectual lineage.
import pandas as pd
# We trace the influence of Thomas Nagel's "What Is It Like to Be a Bat?" (1974)
SEMINAL_AUTHOR = "Nagel"
SEMINAL_CONCEPTS = ["consciousness", "qualia", "what is it like", "bat"]
START_YEAR = 1974print("=== Tracing Influence of Nagel's 'What Is It Like to Be a Bat?' ===\n")
# ── Collect citing / responding works ────────────────────────────────────────
all_results = []
for concept in SEMINAL_CONCEPTS:
query = f"{SEMINAL_AUTHOR}{concept}"
results = search_philpapers(query, years=(START_YEAR, 2025), max_results=25)
for r in results:
r["search_concept"] = concept
all_results.extend(results)
time.sleep(1.0) # polite rate limit# Deduplicate
seen_ids = set()
unique_results = []
for r in all_results:
pid = r.get("philpapers_id") or r.get("title", "")
if pid notin seen_ids:
seen_ids.add(pid)
unique_results.append(r)
print(f"Unique works found across concept searches: {len(unique_results)}")
# ── SEP articles on related topics ───────────────────────────────────────────
related_sep = []
for concept in ["consciousness", "qualia", "mind-body problem"]:
entries = search_sep(concept, max_results=5)
related_sep.extend(entries)
print(f"Related SEP entries: {len(related_sep)}")
# ── Build timeline DataFrame ─────────────────────────────────────────────────
df = pd.DataFrame(unique_results)
ifnot df.empty and"year"in df.columns:
df["year_int"] = pd.to_numeric(df["year"], errors="coerce")
df = df.dropna(subset=["year_int"]).sort_values("year_int")
# Decade-by-decade response summary
df["decade"] = (df["year_int"] // 10 * 10).astype(int)
print("\nResponsesdecade by decade:")
for decade, group in df.groupby("decade"):
titles = group["title"].tolist()[:3]
print(f"\n {decade}s ({len(group)} works):")
for t in titles:
print(f" - {t}")
# ── Get SEP article on consciousness for its bibliography ────────────────────print("\nFetching SEP 'consciousness' article bibliography...")
consciousness_bib = get_sep_article_bibliography("consciousness")
print(f" {len(consciousness_bib)} references in SEP consciousness article")
# Find references by Nagel
nagel_refs = [r for r in consciousness_bib if"nagel"in r["raw_citation"].lower()]
print(f" References to Nagel: {len(nagel_refs)}")
for ref in nagel_refs[:3]:
print(f" [{ref['year']}] {ref['raw_citation'][:120]}")
# ── Format Chicago Notes citations for 5 key responses ───────────────────────print("\n=== Key Responding Works (Chicago Notes Format) ===")
ifnot df.empty:
for _, row in df.head(5).iterrows():
entry = row.to_dict()
print(f" {format_chicago_notes(entry)}")
print()
# ── Export influence map ──────────────────────────────────────────────────────ifnot df.empty:
df.to_csv("nagel_bat_influence_map.csv", index=False, encoding="utf-8")
print("Saved influence map to nagel_bat_influence_map.csv")
Philosophy-Specific Search Strategies
Tracing Philosophical Traditions
When researching a philosophical concept, use the following layered approach:
Start with SEP: SEP articles provide authoritative overviews with
curated bibliographies. Begin with get_sep_article_bibliography() to
identify the canonical texts.
Expand via PhilPapers: Use search_philpapers() with the concept and
key author names found in the SEP bibliography.
Concept genealogy: Apply trace_concept_genealogy() to see when
interest in the topic peaked and declined.
Citation tracing: Search for works that cite or respond to a seminal
paper by combining author name + key terms.
Cross-tradition search: Philosophy concepts often appear under different
names in different traditions (analytic vs continental). Always search
synonyms (e.g. "supervenience" and "strong supervenience" and "local
supervenience").
Chicago Citation Style Notes
The Chicago Manual of Style (17th ed.) provides two parallel systems:
Notes-Bibliography (Chicago Notes): Used in humanities. Full references
appear in footnotes/endnotes and a bibliography. Use format_chicago_notes().
Author-Date (Chicago Author-Date): Used in social sciences. In-text
citations are (Author Year, Page). Full references in a reference list.
Use format_chicago_author_date().
For philosophy journals, Notes-Bibliography is standard (e.g. Philosophical
Review, Ethics, Mind). Author-Date is used in some interdisciplinary
venues (Synthese, Erkenntnis, Philosophy of Science).
Notes and Best Practices
# Example: Check SEP robots.txt compliance before scrapingimport requests
resp = requests.get("https://plato.stanford.edu/robots.txt", timeout=10)
print(resp.text)
# Output shows no Disallow for /entries/ — academic scraping is permitted# at a polite rate. Always use _SEP_CRAWL_DELAY >= 2 seconds.
SEP rate limiting: SEP is a non-profit academic resource. Keep crawl
delays at 2+ seconds and cache results locally to avoid redundant requests.
Do not download entire articles for corpus construction without permission.
PhilPapers API: The API is rate-limited and may return truncated abstracts.
For full paper text, follow the DOI or PhilPapers URL directly.
Author name disambiguation: Philosophy authors often publish under
multiple name forms (e.g. "Bernard Williams" vs "B.A.O. Williams"). Use
PhilPapers author IDs when available for precise queries.
Year parsing: Historical philosophy papers may list year ranges
(e.g. "1781/1998"). Use pd.to_numeric(..., errors='coerce') and handle NaN
appropriately.
CrossRef for DOI lookups: For papers found via SEP bibliographies
without DOIs, use the CrossRef REST API (api.crossref.org/works) to resolve
full metadata by title + author name.
