Literature Scan

---
name: literature-scan
description: Use when starting research on a new topic - produces a one-page map of the canonical 5-10 sources and the major positions
kind: workflow
audience: anyone
ai-tools: any
complexity: guided
time: 60-120 min
version: 1.0.0
source: bundled
---

# Literature Scan

## What this does

Maps an unfamiliar topic by finding the canonical 5-10 sources, identifying the major positions/camps/findings, and producing a one-page brief that you can navigate from. Avoids the "drowning in tabs" problem.

## When to use

- Starting research on a topic you don't know well
- Preparing to write about, present on, or interview someone about a topic
- Choosing what to read deeply (after this scan)
- Building a literature review (this is the first stage)

## What you bring (Inputs)

- The topic, narrowed to a single sentence
- The audience (yourself / a memo reader / a paper reviewer) — drives depth
- Time budget (1 hour for a quick scan, half-day for a thorough one)

## What you get (Output)

A one-page brief with: scope, 5-10 canonical sources (with one-line summaries), the 2-4 major positions or camps, the consensus and the disagreements, and the gaps you noticed.

## How it works (Steps)

This is a workflow.

## Stages

### Stage 1: Define scope precisely

Write the topic in one sentence. If you can't, the scope is too broad. Examples:

- Bad: "Climate policy"
- Good: "Carbon border adjustment mechanisms and their impact on developing-country exporters, 2020-2025"

### Stage 2: Find the canonical sources

For 60-90 minutes, hunt for the 5-10 sources everyone in this area cites. Signals:

- Cited by multiple recent reviews
- Authored by people who appear repeatedly
- The seminal paper that established the field/term
- The most recent comprehensive review

Capture: title, author, year, one-line summary, why it matters.

### Stage 3: Identify the camps

Read the abstracts/introductions of all sources. Group them. Are there 2-4 distinct positions, methodologies, or findings?

Examples: "Pro vs anti," "Empirical vs theoretical," "Three different mechanisms proposed," "Two regional perspectives."

### Stage 4: Note consensus and disagreement

What does almost everyone agree on? What is actively contested? Where does new evidence point?

### Stage 5: Note your gaps

What questions remain unanswered in what you read? What surprised you? What didn't you understand?

These become your reading list for deep-dive.

### Stage 6: Write the brief

One page (or slightly more). Sections: scope, sources, camps, consensus, disagreement, gaps.

## Checkpoints

- **After Stage 1**: scope fits in one sentence
- **After Stage 2**: 5-10 sources, no fewer (gap in canon) and no more (procrastination)
- **After Stage 3**: at least 2 distinct camps named
- **After Stage 6**: brief fits on one page; you could hand it to someone else and they'd know the lay of the land

## Loop-back conditions

- If Stage 2 turns up 0-2 sources: scope is too narrow or you're using the wrong terminology — go back and re-search with synonyms
- If Stage 2 turns up 50+ sources: scope is too broad — narrow it
- If Stage 3 finds no camps: either everyone agrees (rare) or you didn't read enough — re-do

## Quality bar

- Each source has a one-line summary written in your own words
- "Camps" are described, not just listed
- Disagreement is named specifically (not "they disagree on details")
- Brief is one page — if longer, it's a literature review, not a scan

## Variations

- **Pre-interview scan**: scope around what the interviewee has said publicly
- **Pre-decision scan**: scope around the decision options
- **Hot-topic scan**: prioritize 2024-2026 sources

## Example

**Input**: "What are the main approaches to evaluating large language model alignment in 2025?"

**Output (one-page brief)**:

- Scope: ...
- 8 canonical sources: 3 RLHF-era papers, 2 mechanistic-interpretability surveys, 2 evals papers, 1 critique
- 3 camps: behavioral evals, mechanistic interp, scalable oversight
- Consensus: behavioral evals are necessary but insufficient
- Disagreement: whether mechanistic interp can scale to frontier models
- Gaps: how each approach handles agentic use; my next reads